Polynomial-time assignment-based cell association with generic utility functions

An analytical solution for the robust investment-reinsurance strategy with general utilities

Optimal schedule for works running on machines, in a general case, is a hard problem and there is no complete optimal deterministic algorithm in polynomial time. Optimal and approximated solutions were issued for some specific cases with constraints. One can find the solutions for the cases 1 and 2 machines [4,16] as the initial algorithms. In [16], author solved late works problem using algorithm with setup times included. [4] solve the due works problem on two machines, and can be extended for the case of 3 machines with some conditions on works. Other authors looked at schedule problems in specific cases like [5,8,17]. In this paper, we apply duration automata [1,2] to solve the schedule problem dynamically for the works with uncertain processing time in a cluster computer, which is a system consisting of many computers (computing nodes) co-working together. We solve the schedule problem in a cluster with $m$ machines for two cases: all machines are the same and different in configurations (machine's resources are formally considered as a configuration information, represented by the time need to finish the works). Because of uncertainly processing time, tranditional algorithms can not be used effectively. By using DA model with DA's order criterion, we issue schedule algorithms and practically prove to be better in time consuming compare to FIFO (natural order), the fastest first (greedy) and the longest first (safety) methods without synchronized points.

In this paper, we analytically solve the utility maximization problem for a consumption set with multiple habit formation of interaction. Consumption is here composed of habitual and nonhabitual components, where habitual consumption represents the effect of past consumption. We further assume that the individual seeks to maximize his/her expected utility from nonhabitual consumption. Although there is usually no explicit solution of linear dynamic systems in the habit formation model, we study the functional minimum of habitual consumption. To solve the optimization problem with a general utility function, we adopt the convex dual martingale approach to construct the optimal consumption strategy and provide an economic interpretation for nearly every object throughout the solution process.

We study computational complexity theory and define a class of optimization problems called OptP (Optimization Polynomial Time), and we show that TRAVELLING SALESPERSON, KNAPSACK and 0-1 INTEGER LINEAR PROGRAMMING are complete for OptP. OptP is a natural generalization of NP (Nondeterministic Polynomial Time), but while NP only considers problems at the level of their yes/no question, the value of an OptP function is the optimal value of the problem. This approach enables us to show a deeper level of structure in these problems than is possible in NP. OptP is a subset of FPSAT, the class of functions computable in polynomial time with an oracle for NP. Our central result is that any FPSAT function decomposes into an OptP function followed by polynomial-time computation. The significance of this result is that it quantifies how much NP-completeness is in a problem, i.e., the number of NP queries it takes to compute the function. It also allows us to unify the classes NP, DP and DELTA-2 in a natural way. For example, we prove that an OptP-completeness result implies, as corollaries, NP-, DP- and DELTA-2- completeness results. We also prove separation results on subclasses of FPSAT by restricting the number of calls to the NP oracle. For example, TRAVELLING SALESPERSON is complete for O(n) queries, CLIQUE is complete for O(log n) queries and BIN PACKING can be solved in O(log log n) queries. We prove these classes distinct under the assumption that P does not equal NP. Finally, we consider generalizations of OptP to higher levels in the Polynomial-Time Hierarchy. We define the DOUBLE KNAPSACK problem and prove that it is complete for DELTA-3, the first example of a natural complete problem for this class, and the highest level in the Polynomial Hierarchy with a known natural complete problem.

The rate distribution in heterogeneous networks (HetNets) greatly benefits from load balancing, by which mobile users are pushed onto lightly-loaded small cells despite the resulting loss in SINR. This offloading can be made more aggressive and robust if the macrocells leave a fraction of time/frequency resource blank, which reduces the interference to the offloaded users. We investigate the joint optimization of this technique - referred to in 3GPP as enhanced intercell interference coordination (eICIC) via almost blank subframes (ABSs) - with offloading in this paper. Although the joint cell association and blank resource (BR) problem is nominally combinatorial, by allowing users to associate with multiple base stations (BSs), the problem becomes convex, and upper bounds the performance versus a binary association. We show both theoretically and through simulation that the optimal solution of the relaxed problem still results in an association that is mostly binary. The optimal association differs significantly when the macrocell is on or off; in particular the offloading can be much more aggressive when the resource is left blank by macro BSs. Further, we observe that jointly optimizing the offloading with BR is important. The rate gain for cell edge users (the worst 3-10%) is very large - on the order of 5-10x - versus a naive association strategy without macrocell blanking.

SummaryThe content‐based image retrieval (CBIR) has been applied in the image processing as well as pattern recognition. A challenging task in the CBIR research has been through the feature extraction for decreasing any semantic gap that is an active research topic. Here, in this work, there is a texture feature that is extracted from an image, making use of a technique of curvelet transform. This curvelet is selected for a sparse representation that is quite critical for the estimation of images, which have been de‐noised with some inversed problems. The wrapping‐based curvelet transform will be even more robust as well as faster in the time of computation than the Ridge Transform. A technique of feature selection will be brought for selecting the optimal features. The correlation‐based feature selection (CFS) method has been adapted for improving the accuracy of the CBIR systems. The non‐deterministic polynomial (NP)–hard problems may be solved using the chemical reaction optimization (CRO) that has been motivated using the technique of chemical reaction. The benefits of proposed method is a necessity for marginal human intrusion for the purpose of retrieving the images needed from its database. The proposed mechanism has been again assessed depending on the Coral Database, and a performance study is ended using precision, f measure, and recall.

Introduction The Common Lisp Object System is an object-oriented extension to Common Lisp as defined in Common Lisp: The Language, by Guy L. Steele Jr. It is based on generic functions, multiple inheritance, declarative method combination, and a meta-object protocol. The first two chapters of this specification present a description of the standard Programmer Interface for the Common Lisp Object System. The first chapter contains a description of the concepts of the Common Lisp Object System, and the second contains a description of the functions and macros in the Common Lisp Object System Programmer Interface. The chapter "The Common Lisp Object System Meta-Object Protocol" describes how the Common Lisp Object System can be customized. The fundamental objects of the Common Lisp Object System are classes, instances, generic functions, and methods. A class object determines the structure and behavior of a set of other objects, which are called its instances. Every Common Lisp object is an instance of a class. The class of an object determines the set of operations that can be performed on the object. A generic function is a function whose behavior depends on the classes or identities of the arguments supplied to it. A generic function object contains a set of methods, a lambda-list, a method combination type, and other information. The methods define the class-specific behavior and operations of the generic function; a method is said to specialize a generic function. When invoked, a generic function executes a subset of its methods based on the classes of its arguments. A generic function can be used in the same ways that an ordinary function can be used in Common Lisp; in particular, a generic function can be used as an argument to funcall and apply and can be given a global or a local name. A method is an object that contains a method function, a sequence of parameter speclalizers that specify when the given method is applicable, and a sequence of qualifiers that is used by the method combination facility to distinguish among methods. Each required formal parameter of each method has an associated parameter specializer, and the method will be invoked only on arguments that satisfy its parameter specializers. The method combination facility controls the selection of methods, the order in which they are run, and the values that are returned by the generic function. The Common Lisp Object System offers a default method combination type and provides a facility for declaring new types of method combination.

Partition of networks into optimal set of clusters is the prominent technique to prolong the network lifetime of energy constrained wireless sensor networks. Enumeration search method cannot find optimal clusters within polynomial bounded time for large scale networks since the computational complexity of problem grows exponentially with the dimension of networks. Optimal cluster configuration in sensor networks is known to be Non-deterministic Polynomial (NP)-hard optimization problem and for that reason we have applied polynomial time metaheuristic algorithms to find optimal or near-optimal solutions. In this paper, we present clustering algorithms based on Simulated Annealing (SA) and Particle Swarm Optimization (PSO) to find optimal set of cluster heads in the network. The optimization problem consists of finding optimal configuration of clusters such that the communication distance per cluster is not only minimized but the cluster balance and energy efficiency is also maintained in the network. The SA and PSO toolboxes are developed in C++ and integrated with OMNeT++ simulation environment to implement the proposed clustering algorithms. The performance of algorithms with respect to network lifetime, load balance and energy efficiency of network is examined in the simulation.

Lattice-based cryptography has recently emerged as a prime candidate for efficient and secure post-quantum cryptography. The two main hard problems underlying its security are the shortest vector problem (SVP) and the closest vector problem (CVP). Various algorithms have been studied for solving these problems, and for SVP, lattice sieving currently dominates in terms of the asymptotic time complexity: one can heuristically solve SVP in time $2^{0.292d}$ in high dimensions $d$ [BDGL'16]. Although several SVP algorithms can also be used to solve CVP, it is not clear whether this also holds for heuristic lattice sieving methods. The best time complexity for CVP is currently $2^{0.377d}$ [BGJ'14]. In this paper we revisit sieving algorithms for solving SVP, and study how these algorithms can be modified to solve CVP and its variants as well. Our first method is aimed at solving one problem instance and minimizes the overall time complexity for a single CVP instance with a time complexity of $2^{0.292d}$. Our second method minimizes the amortized time complexity for several instances on the same lattice, at the cost of a larger preprocessing cost. We can solve the closest vector problem with preprocessing (CVPP) with $2^{0.636d}$ space and preprocessing, in $2^{0.136d}$ time, while the query complexity can even be reduced to $2^{\epsilon d}$ at the cost of preprocessing time and memory complexities of $(1/\epsilon)^{O(d)}$. For easier variants of CVP, such as approximate CVP and bounded distance decoding (BDD), we further show how the preprocessing method achieves even better complexities. For instance, we can solve approximate CVPP with large approximation factors $k$ with polynomial-sized advice in polynomial time if $k = \Omega(\sqrt{d/\log d})$, heuristically closing the gap between the decision-CVPP result of [AR'04] and the search-CVPP result of [DRS'14].

The rapidly increasing amounts of mobile traffic have brought new challenges on the limited spectrum resource. LTE on the unlicensed band, namely, LTE-U, under the carrier aggregation technology has been considered as a potential solution to handle the increasing demand for high data rate that could provide a higher transmission data rate, spectrum efficiency, as well as seamless mobile user experience. To ensure the fairness access between LTE-U BSs in unlicensed bands, in this paper, we consider the multiple LTE-U base stations (BSs) in the same interference region sharing unlicensed bands. We first analyze the hybrid full-duplex/half-duplex transmission mode of LTE-U BSs based on the network status. Although there is throughput gain from the full-duplex transmission, it cannot always provide a superior performance than the half-duplex transmission mode due to the influence of the self-interference. In addition, we further extensively investigate the opportunistic multiple LTE-U BSs access scheme on the unlicensed band based on the channel bidding mechanism to guarantee the opportunistic harmonious sharing on unlicensed bands among LTE-U BSs. Third, a bidding-based dual-band resource allocation scheme is proposed with dynamic hybrid transmission mode to achieve the maximum network utility while guaranteeing the packet delay constraint and minimizing the packet drop rate under the Lyapunov optimization. Finally, various simulation results demonstrate that the algorithm we proposed could achieve a considerable performance improvement with respect to the schemes in the literature.

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm.

In order to realize high precision comparison between the time of remote user and UTC (NTSC), the National Time Service Center has built a set of high accuracy remote time common-view system based on the satellite common view time transfer method, the underlying system comprised with a base station and some calibration stations and a data processing center. Among them, the base station provides a reference measurement value for the whole system, namely the bias of UTC (NTSC) and the satellite clock. The calibration stations are used to provide a measurement value of the measured stations, namely the bias of local time of calibration station and the satellite clock. The data processing center is responsible for completing the time deviation between the local time of calibration stations and UTC (NTSC). In view of the importance of base station in the system, this paper has designed two kinds of configuration scheme, the single base station and multiple base stations. According to this a large number of tests has been analyzed. The results of experiment describe the single base station is easy to use, its timing accuracy is about 10ns, multi-base stations with higher availability and reliability, and its timing accuracy is 3 ∼ 5ns.

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

Massive multiple-input multiple-output (MIMO) technology is expected to achieve significant gains in both signal-to-noise-plus-interference ratio (SINR) and throughput for 5G cellular wireless networks. Efficient and highly accurate channel state information (CSI) acquisition at the base stations (BS) is essential to achieve the potential benefits of massive MIMO systems. However, the inadequate number of orthogonal pilot sequences used for CSI estimation leads to erroneous channel estimation as it causes interference between pilot sequences. This phenomenon is coined pilot contamination and severely limits the system performance. Therefore, we address in this paper the pilot contamination problem in massive MIMO Cloud-Radio Access Networks systems. Leveraging on a real telecom operator data delivered by Call Detail Records (CDR), our objective is to maximize the average uplink achievable rate by reducing the effect of pilot contamination in massive MIMO Cloud-Radio Access Networks (C-RAN). As such a problem is a non-linear integer problem that has no solution in polynomial time, we develop a two-stage solution to solve it. First, a coalition game is proposed where Remote Radio Heads (RRHs) gather into clusters with random user-pilot allocation. Then, two greedy heuristics are applied to match each user in the cluster with a given pilot. The goal is to further improve the average uplink rate achieved in the first stage. Simulation results show that our heuristic solutions for pilot contamination mitigation outperform the traditional pilot allocation solution and a state-of-the-art pilot allocation scheme based on large-scale fading in terms of average uplink achievable rate and SINR.

Prediction of future traffic load is a crucial task to support the automatic Operations, Administration, and Management (OAM) of communication networks. Existing Machine Learning (ML) models require big data to accomplish this task. However, large data sets are not always available, due to the limited storage capacity and the high storage cost at Base Stations (BSs). To solve the problem, we leverage the spatial-temporal correlation among different BSs, which allows other BSs’ data to be used for the prediction of the target BS. One major challenge in realizing this idea is the imbalance of data amounts between neighbor BSs and a prediction target BS. If one simply aggregates the data from both neighbors and target, the target’s traffic features would be overwhelmed by the neighbors’ data. To address this challenge, we propose a Spatial-Temporal Transfer (STT) framework, which trains a base model with an aggregated data set from multiple BSs, and then carefully refines the base model to serve a target BS. To strike a perfect balance between general tendency and individual features, STT adopts an advanced transfer learning technique that exploits regularization on model parameters. Experiments show the efficiency of the proposed STT framework.