Alternative Search Method Research Articles

Empirical Testing of Alternative Search Methods to Retrieve Utility Values for Health Economic Modelling.

The objective of this study is to compare different information retrieval methods that can be used to identify utility inputs for health economic models. Theusual practice of using systematic review methods was compared with two alternatives (iterative searching and rapid review), using a health technology assessment (HTA) case study in ulcerative colitis (UC). We analysed whether there were differences in the utility values identified when using the alternative search methods. Success was evaluated in terms of time, burden and relevance of identified information. The identified utility values were tested in an executable health economic model developed for UC, and the model results were compared. The usual practice of using systematic review search approaches identified the most publications but was also the least precise method and took longest to complete.The inclusion of data from the different search methods in the model did not lead to different conclusions across search methods. In this case study, usual practice was less efficient and resulted in the same health economic model conclusions as the alternative search methods. Further case studies are required to examine whether this conclusion might be generalisable.

An Alternate Search Method and An Extended Trace Line Method for Wheel‐Rail Contact Patch Centers Detection

AbstractWheel‐rail contact is a link between the vehicle system and the rail system, and the contact processing approach affects accuracy of vehicle dynamic simulation. Contact detection is usually the start of wheel‐rail contact calculation, and detecting precision of contact patch center affect accuracy of contact force calculating results, therefore it is significant to improve detecting precision of contact patch center. An alternate search method (ASM) and an extended trace line method are proposed to improve precision and efficiency of contact patch centers detection. The proposed alternate search method applies Golden Ratio Search Method and variable intervals between grids to detect contact patch centers. To make the detected contact patch centers match rail tangential directions better, an extended trace line method is constructed based on geometrical constraints between contact points and rail tangential directions. Dynamic simulations of a bogie vehicle formed by two wheelsets and a bogie frame negotiating a rail curve are conducted to evaluate effects of the proposed methods. Calculating times of the proposed methods for finishing the simulations are compared to explain computational efficiencies of the different methods. The proposed approaches are real‐time capable and can be used for vehicle dynamics simulations.

Computation bits enhancement for IRS-assisted multi-UAV wireless powered mobile edge computing systems

Mobile edge computing (MEC) and wireless energy transfer are two important paradigms to improve the computation bits and provide more cost-effective energy for low-power internet of things (IoT) devices. However, the performance of energy transfer and task offloading is significantly affected by propagation loss, and hence the links between IoT devices and transmitters are frequently blocked. To address this issue, in this paper, an intelligent reflecting surfaces (IRS) system is utilized to enhance the energy harvesting and task offloading performances in a multi-unmanned aerial vehicle (UAV)-assisted MEC system. Also, a power beacon (PB) is deployed in the coverage area to provide suitable and stable energy for UAVs via a laser charging system. The sum computation bits of all IoT devices are maximized by jointly optimizing transmit power, time allocation, the phase shift in the energy transfer stage, the phase shift in the task offloading stage, CPU frequency of IoT devices, and UAVs' trajectory. An alternate search method (ASM) is proposed to tackle the non-convexity of the formulated problem in which the closed-form expressions of transmit power, time allocation, the phase shift in the task offloading stage, and CPU frequency are derived. Also, the semidefinite relaxation (SDR) approach and successive convex approximation (SCA) methods are exploited to obtain the phase shifts in the energy transfer stage and UAVs' trajectory via CVX optimizer, respectively. The numerical evaluations confirm the performance gain of the proposed IRS-assisted multi-UAV MEC system compared to existing benchmark designs.

Multiplexing Capacity of Hybrid PD-SCMA Heterogeneous Networks

Hybrid multiple access schemes are considered potential technologies towards achieving optimal spectrum sharing for future heterogeneous networks. Multiple users are multiplexed on a single resource unit in the code domain for sparse code multiple access (SCMA) or power domain for power domain non-orthogonal multiple access (PD-NOMA) and in both domains for the hybrid power domain sparse code non-orthogonal multiple access (PD-SCMA). This allows for effective spectrum usage but comes at a cost of increased detection complexity resulting in loss of performance in terms of outages. It is therefore imperative to determine the user multiplexing capacity for effective performance. This work investigates codebook capacity bounds in small cells, pairing and power capacity bounds for the number of small cell user equipment’s (SUEs) and macro cell user equipment’s (MUEs) that can be multiplexed on a codebook for the developed PD-SCMA technology. Closed-form solutions for codebook, pairing and power multiplexing capacity bounds are derived. The performance of the system results into low outage when the system’s point of operation is within the multiplexing bounds. To alleviate the resource allocation (RA) challenges of such a system at the transmitter, dual parameter ranking (DPR) and alternate search method (ASM) based RA schemes are proposed. The results show significant capacity gain with DPR-RA in comparison with conventional schemes.

Multicasting in NOMA‐based UAV networks: Path design and throughput maximisation

In this paper, a new resource allocation framework for unmanned aerial vehicle (UAV) assisted multicast wireless networks is proposed in which the network users according to their request are divided into several multicast groups. Power domain non-orthogonal multiple access is adopted as the transmission technology using which the dedicated signals of multicast groups are superimposed and transmitted simultaneously as the UAV passes over the communication area for fixed and mobile users. The proposed scenarios are discussed from two perspectives, offline and online modes. In offline mode, the problem is implemented for fixed and mobile users whose locations are predictable (the location of users, over the communication time, is known at the beginning of the communication time) and in online mode for mobile users whose locations are unpredictable (the location of users, over the communication time, is unknown at the beginning of the communication time). Also, a scenario is proposed in which the online mode the number of mobile users can grow in each time slot. The problem of joint power allocation and UAV trajectory design as an optimisation problem that is non-linear and non-convex for two proposed scenarios is formulated. To solve the problem, an alternate search method, successive convex approximation, and geometric programming are adopted. Using simulation results, the performance of the proposed scheme is evaluated for different values of the network parameters.

Learning adaptive weighted response consistency correlation filters for real-time UAV tracking

Due to the high efficiency of discriminative correlation filter (DCF), it has attracted widespread attention in the field of UAV object tracking. To handle the problem of filter degradation, many trackers usually introduce temporal regularization to enhance the discriminative power of the filter. However, these temporal regularization methods only utilize the limited information between two consecutive frames, which are susceptible interference by previous corrupted information. Besides, regularization terms with predefined hyperparameters can not well adapt to the variations of the target across sequent frames, which may cause the model degradation or drift. We propose a tracker based on DCF framework to fully exploit the information hidden in the historical response map, namely adaptive weighted response consistency-based DCF tracking. Specifically, carefully selected historical response maps with fixed weight distribution are introduced in training phase to increase the robustness of the filter. Further, we present a unified loss for jointly learning the filter and the weight distribution, which can be solved by the alternate convex search method. The joint loss guarantees that reliable response maps contribute more to filter learning, leading to a more discriminative and adaptive filter for tracking the target. Extensive experiments show that the proposed method achieves state-of-the-art results on two datasets.

Adaptation or Exaptation? Search and Resource Redeployment as a Response to Environmental Shocks

Environmental shocks can significantly alter the organization’s opportunity landscape and trigger the search for new solutions. However, adaptive search is frequently insufficient to recover from a major disruptive shock. We analyze an alternative search method, exaptive search – applying existing solutions to new problems – as a potentially useful response to such shocks, using an NK model of exaptive and adaptive search. We find that the optimal search response to disruptive shocks is that of an initial phase of exaptive search, wherein organizations repurpose their existing resource bundles to address the suddenly changed needs of the business environment, followed by adaptive search to adjust these resource bundles to their new uses. We further find that exaptive search is particularly fruitful in relatedly diversified organizations.

Joint sub-carrier allocation and 3D beamforming design in OMA-NOMA based mmWave heterogeneous networks under channel uncertainties

In this paper, we investigate a new radio resource allocation, three-dimensional (3D) multiple input single output (MISO) beamforming and cross-tier interference mitigation in a millimeter wave (mmWave) heterogeneous cellular network. To manage the cross-tier interference, a scheme based on the combination of orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) is proposed. To this end, the total bandwidth is divided into OMA-based and NOMA-based sub-carriers in which the macro-cell base station (MBS) uses OMA-based sub-carriers and the total bandwidth is considered for small cell base stations (SBSs). For maximizing the sum-rate of OMA-NOMA based system, a non-convex optimization problem is formulated where the channel state information (CSI) is not known perfectly. The proposed optimization problem is solved by introducing auxiliary variables, alternate search method (ASM) and successive convex approximation (SCA) technique. Then, an iterative algorithm is adopted where sub-carrier, beamforming and BS tilt angle sub-problems are sequentially obtained. The simulation results confirm the effectiveness of the proposed scheme compared with 2D beamforming and OMA-based schemes.

Secrecy-enhancing design for two-way energy harvesting cooperative networks with full-duplex relay jamming

To guarantee secure information transmission within two sources, a full-duplex (FD) relay jamming scheme for two-way energy harvesting (EH) cooperative networks is proposed, in which the relay can achieve simultaneous information reception and jamming transmission. The EH relay node scavenges energy from two source signals, and then, uses the harvested energy for information relaying and jamming transmission by the power splitting (PS) protocol. Considering multiple eavesdroppers, the non-convex optimization for maximizing the smaller bidirectional secrecy rates is formulated by jointly optimizing the power allocation and PS factor. Furthermore, an iterative algorithm based on successive convex approximation and alternate search method is proposed to solve this non-convexity. Simulation results verify the proposed iterative algorithm and show that the proposed jamming scheme can achieve substantial performance gain than other conventional schemes.

Joint Radio Resource Allocation and Cooperative Caching in PD-NOMA-Based HetNets

In this paper, we propose a novel joint resource allocation and cooperative caching scheme for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous networks (HetNets). In our scheme, the requested content is fetched directly from the edge if it is cached in the storage of one of the base stations (BSs), and otherwise is fetched via the backhaul. Our scheme consists of two phases: 1) Caching phase where the contents are saved in the storage of the BSs; and 2) Delivery phase where the requested contents are delivered to users. We formulate a novel optimization problem over radio resources and content placement variables. We aim to minimize the network cost subject to quality-of-service (QoS), caching, subcarrier assignment, and power allocation constraints. By exploiting advanced optimization methods, such as alternative search method (ASM), Hungarian algorithm, successive convex approximation (SCA), we obtain an efficient sub-optimal solution of the optimization problem. Numerical results illustrate that our ergodic caching policy via the proposed resource management algorithm can achieve a considerable reduction on the total cost on average compared to the most popular caching and random caching policy. Moreover, our cooperative NOMA scheme outperforms orthogonal multiple access (OMA) in terms of the delivery cost in general with an acceptable complexity increase.

E2E QoS Guarantee for the Tactile Internet via Joint NFV and Radio Resource Allocation

The Tactile Internet (TI) is one of the next generation wireless network services with end to end (E2E) delay as low as 1 ms. Since this ultra low E2E delay cannot be met in the current 4G network architecture, it is necessary to investigate this service in the next generation wireless network by considering new technologies such as networks function virtualization (NFV). On the other hand, given the importance of E2E delay in the TI service, it is crucial to consider the delay of all parts of the network, including the radio access part and the NFV core part. In this paper, for the first time, we investigate the joint radio resource allocation (R-RA) and NFV resource allocation (NFV-RA) in a heterogeneous network where queuing delays, transmission delays, and delays resulting from virtual network function (VNF) execution are jointly considered. For this setup, we formulate a new resource allocation (RA) problem to minimize the total cost function subject to guaranteeing E2E delay of each connection. Since the proposed optimization problem is highly non-convex, we exploit alternative search method (ASM), successive convex approximation (SCA), and heuristic algorithms to solve it. Besides, for the NFV-RA, we propose an online heuristic algorithm, and analyze its performance for the TI service. Simulation results reveal that the proposed scheme can significantly reduce the network costs compared to the case where the two problems are optimized separately. Moreover, we compare the online algorithm with its offline counterpart as well as a baseline approach and it is shown that the online algorithm outperforms both of them.

Bayesian Semi-Parametric Realized Conditional Autoregressive Expectile Models for Tail Risk Forecasting

AbstractA new model framework called Realized Conditional Autoregressive Expectile is proposed, whereby a measurement equation is added to the conventional Conditional Autoregressive Expectile model. A realized measure acts as the dependent variable in the measurement equation, capturing the contemporaneous dependence between it and the latent conditional expectile; it also drives the expectile dynamics. The usual grid search and asymmetric least squares optimization, to estimate the expectile level and parameters, suffers from convergence issues leading to inefficient estimation. This article develops an alternative random walk Metropolis stochastic target search method, incorporating an adaptive Markov Chain Monte Carlo sampler, which leads to improved accuracy in estimation of the expectile level and model parameters. The sampling properties of this method are assessed via a simulation study. In a forecast study applied to several market indices and asset return series, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed model class.

Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era.

One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward.

Secure transmission for energy harvesting relay networks with the destination self-protection mechanism

In this paper, we investigate the physical layer security (PLS) of an energy harvesting (EH) cooperative network with the destination self-protection mechanism. More specifically, we consider a cooperative transmission scenario in which a source communicates with a destination through a wireless-powered relay using power splitting (PS) protocol in the presence of an eavesdropper. To degrade the wiretap channel, the destination is operated in full-duplex (FD) to achieve simultaneous information reception and jamming transmission. Taking into account the residual self-interference (SI) at the destination, the non-convex secrecy rate maximization problem is formulated to jointly optimize the PS factor and power allocation of source and destination. Due to the non-convexity of the joint optimization, a low complexity iterative algorithm based on the alternative search method and difference of convex functions (DC) programming is proposed. The original problem is first decomposed into two sub-problems with the fixed transmission power or PS factor. Since the decomposed two sub-problems are still non-convex, we utilize the DC programming to obtain the local solutions of the two sub-problems. Then the global solutions can be obtained by the alternative search method. Simulation results present that the proposed scheme has fast convergence. Comparing with other conventional schemes, the proposed scheme has the best security performance.

Joint Radio Resource Allocation and SIC Ordering in NOMA-Based Networks Using Submodularity and Matching Theory

In this paper, we propose a novel joint radio resource allocation and successive interference cancellation (SIC) ordering algorithm for the downlink scenario of a power-domain sparse code multiple access (PSMA) based wireless network. To this end, we formulate a novel joint radio and SIC ordering optimization problem. Our aim is to maximize sum rate over codebook assignment, transmit power, and SIC ordering variables. Since the proposed optimization problem is NP-hard and mathematically intractable, we exploit alternate search method (ASM). Based on this algorithm, the proposed optimization problem is decomposed into three sub-problems, namely, codebook assignment, power allocation, and SIC ordering. To solve the codebook assignment sub-problem, we exploit matching theory and submodularity and compared them from the performance and complexity perspectives. Due to the fact that PD-NOMA-based networks adopt SIC at the receiver side, the proposed SIC ordering algorithm is also studied for this type of networks. Moreover, the proposed algorithms are investigated from the performance, convergence, and computational complexity perspectives. Numerical results show that the proposed SIC ordering method outperforms the conventional one by approximately 48% in the studied NOMA-based networks.

SYSTEM APPROACH TO BUILDING MODEL OF FINANCIAL MECHANISM OF STATE-PRIVATE PARTNERSHIP

The organization of financial relations between the state and business in the structure of the financial mechanism of public-private partnership (hereinafter – PPP) is carried out according to certain models. The choice of the model of this mechanism is a way of consolidating the partnership relations of the state with the subject of economic activity. The state is not only the initiator of the creation of certain structural elements of the mechanism, but also focuses on the economic interests of private business and the needs of the population in order to reduce unproductive costs.The purpose of the article is to reveal the necessity of using a systematic approach in the process of constructing a model of the financial mechanism of public-private partnership.Provision of a clear public-private partnership requires the formation of a sufficient financial base, optimization of internal and external financial flows, which necessitates the clarification of the essence and scientific approaches to building PPP financial mechanism models. We believe that the model of the financial mechanism of public-private partnership is a tried, effective and effective way of structuring the elements of this mechanism aimed at the formation of financial resources, their distribution and use, as well as obtaining socio-economic effects and financial returns on realized investments. Advantages of this simulation are: the separation of the main features from secondary, internal from external, constantly repeated from random; determination of the regularities of functioning of the mechanism, which makes it possible to make accurate forecasts; defining the structural components of the mechanism and establishing interrelationships between them in order to balance the interests of the partners.The basis of forming a model of financial mechanism of public-private partnership is the system approach.The system approach considers the financial mechanism of public-private partnership as an integral structure consisting of relatively separate and interconnected subsystems and elements with special specific properties. In this case, the change in one of the elements of any subsystem causes changes in other elements, which is based on the dialectical approach to the interconnection and interconnectedness of all phenomena in nature and society. Such an approach was studied by such well-known scientists as P. Anohin (theory of functional systems), I. Ansoff and L. Fahey («cascading» approach – an alternative search method), S. Bir («black box» method), and others.Thus, the peculiarities of the system approach to the formation of the model of the financial mechanism of public-private partnership are as follows:firstly, the PPP financial mechanism is considered as a coherent structure influenced by environmental factors. In turn, the latter is characterized by uncertainty and variability, incompleteness of information, concealed threats and challenges;secondly, the orientation of the mechanism for prediction, preventive actions, and not the response after what happened, post-factum (elimination of consequences as a result of adaptation to the environment);thirdly, in order to ensure the effective functioning of the mechanism under such conditions, it is necessary for the partners to develop their optimal model, which would match their interests, the ultimate goals and methods of achievement. At the same time, the goals should be correlated with the financial resources, capabilities, potential of the partners;fourth, the financial mechanism provides not only the achievement of the planned tasks, but also its qualitative change, caused by the needs of the environment.

Robust Physical Layer Security for Power Domain Non-Orthogonal Multiple Access-Based HetNets and HUDNs: SIC Avoidance at Eavesdroppers

In this paper, we investigate the physical layer security in downlink of Power Domain Non-Orthogonal Multiple Access (PD-NOMA)-based heterogeneous cellular network (HetNet). In this paper, we assume two categories of users are available: 1) Trusted users and 2) untrusted users (eavesdroppers) at which transparency of users is not clear for the BSs, i.e., they are potential eavesdroppers. Our aim is to maximize the sum secrecy rate of the network. To this end, we formulate joint subcarrier and power allocation optimization problems to increase sum secrecy rate. Moreover, we propose a novel scheme at which the eavesdroppers are prevented from doing Successive Interference Cancellation (SIC), while legitimate users are able to do it. In practical systems, perfectly availability of all eavesdroppers' Channel State Information (CSI) at legitimate transmitters are impractical. Also CSIs of legitimate users may be also imperfect due to the error of channel estimation. Hence, we study two cases of CSI availability: 1) Perfect CSI of nodes (legitimate users and eavesdroppers) are available at the BSs and 2) imperfect CSI of nodes are available at the BSs. Since the proposed optimization problems are non-convex, we adopt the well-known iterative algorithm called Alternative Search Method (ASM). In this algorithm, the optimization problems are converted to two subproblems, power allocation and subcarrier allocation. We solve the power allocation problem by the Successive Convex Approximation approach and solve the subcarrier allocation subproblem, by exploiting the Mesh Adaptive Direct Search algorithm (MADS). Moreover, in order to study the optimality gap of the proposed solution method, we apply the monotonic optimization method. Moreover, we evaluate the proposed scheme for secure massive connectivity in Heterogeneous Ultra Dense Networks (HUDNs). Furthermore, we investigate multiple antennas base stations scenario in this literature. Finally, we numerically compare the proposed scheme with the conventional case at which the eavesdroppers are able to apply SIC. Numerical results highlight that the proposed scheme significantly improves the sum secrecy rate compared with the conventional case.

Joint Access and Fronthaul Radio Resource Allocation in PD-NOMA-Based 5G Networks Enabling Dual Connectivity and CoMP

In this paper, fifth-generation (5G) cellular networks under three promising technologies, namely, dual connectivity, coordinated multi-point transmission (CoMP), and power domain non orthogonal multiple access (PD-NOMA) are investigated. The main aim is to maximize the downlink energy efficiency (EE) by using both millimeter wave (mmW) and micro wave ( $\mu \text{W}$ ) links in access and fronthaul, while employing CoMP and PD-NOMA. In this regard, joint access and fronthaul radio resource allocation for a downlink heterogeneous cloud radio access network is considered. The proposed optimization is a mixed integer non-convex problem with a high computational complexity solution, and hence, the alternate search method based on a successive convex approximation approach using fractional programing is exploited. Furthermore, the convergence of the proposed iterative resource allocation method is proved and its computational complexity is investigated. As the numerical results show, via dual connectivity through receiving signals from both mmW and $\mu \text{W}$ transmitters, the system EE is improved by approximately 50%, in contrast to using only $\mu \text{W}$ subcarriers (e.g., as in local thermal equilibrium). In addition, by applying both PD-NOMA and CoMP technologies on the $\mu \text{W}$ subcarriers, the EE of the system increases by approximately 45%.

Sequential Subspace Clustering via Temporal Smoothness for Sequential Data Segmentation.

This paper develops a novel sequential subspace clustering method for sequential data. Inspired by the state-of-the-art methods, ordered subspace clustering, and temporal subspace clustering, we design a novel local temporal regularization term based on the concept of temporal predictability. Through minimizing the short-term variance on historical data, it can recover the temporal smoothness relationships in sequential data. Moreover, we claim that the local temporal regularization is more important than the global structural regularization for a specific task, such as sequential subspace clustering, which leads to a concise minimization objective function. To solve the bi-convex objective function, a simple and efficient optimization algorithm based on the alternate convex search method is devised to jointly learn the coding matrix and the dictionary. Furthermore, five baseline methods are also devised for comparison with our proposed method from different aspects. Extensive experimental results and comparisons with the state-of-the-art methods on three data sets demonstrate the effectiveness of the proposed temporal smoothness sequential subspace clustering method for sequential data.

SAR Image Reconstruction From Undersampled Raw Data Using MaximumA PosterioriEstimation

A method for synthetic aperture radar (SAR) imaging using maximum a posteriori (MAP) estimation based on multiplicative speckle model is presented. The new method uses the total variation (TV) minimization to regularize the solution. The reconstruction of SAR image is formulated as a biconvex optimization problem, which is solved by the alternate convex search (ACS) method. Experiments on Radarsat-1 raw data show that the proposed method can recover most of the structural and texture details of the imaged scene using only a half of raw data. Compared with regular regularization methods for SAR imaging with incomplete data, the proposed method performs much better on less sparse scenes.