Adaptive Allocation Research Articles

空间调制无线光多输入多输出通信系统中自适应功率分配算法

空间调制无线光多输入多输出通信系统中自适应功率分配算法

Adaptive Cache Allocation with Prefetching Policy over End-to-End Data Processing

With the speed gap between storage system access and processor computing, end-to-end data processing has become a bottleneck to improve the total performance of computer systems over the Internet. Based on the analysis of data processing behavior, an adaptive cache organization scheme is proposed with fast address calculation. This scheme can make full use of the characteristics of stack space data access, adopt fast address calculation strategy, and reduce the hit time of stack access. Adaptively, the stack cache can be turned off from beginning to end, when a stack overflow occurs to avoid the effect of stack switching on processor performance. Also, through the instruction cache and the failure behavior for the data cache, a prefetching policy is developed, which is combined with the data capture of the failover queue state. Finally, the proposed method can maintain the order of instruction and data access, which facilitates the extraction of prefetching in the end-to-end data processing.

Towards Dynamic and Adaptive Allocation of Staff in a Digital-organized Production Context: An Innovative Perspective from Work Science

The increasingly complex socio-technical systems in modern businesses are forcing us to adapt our risk management models to a context of digital-organized production. This new phase of industrial digitization is bringing new opportunities and new challenges for managers and workers. A new work organization is emerging, along with new models for optimizing the allocation of labor to production activities. Three approaches are proposed in the literature: Mathematical optimization, models from work sciences, and integration of these two. Integrated analytical models are limited both by the number of variables considered and knowledge of the associated uncertainties. These models are static, not always focused on reducing risks at the source, and based on expert elicitation by unspecified protocols. Advances in this field are raising several questions regarding ethics, legality and safety. A model that allows systematic reduction of occupational health and safety risks and dynamic integration of operational considerations is needed. Successful businesses in the future will rely on managerial approaches that are interdisciplinary, focused on human wellbeing and draw on smart technologies and artificial intelligence.

Finding the Winner of a Hypothesis Test via Sample Allocation Optimization

This paper studies the optimal adaptive sample allocation for finding the best among a finite set of options with the highest statistical guarantees and in the quickest time. With the growth in the number of experiments that companies run, the existence of an optimal allocation algorithm that reduces the required experimentation time (and hence the cost) while providing statistical confidence in the result is crucial. Old frequentist hypothesis testing methods are primary methods for addressing this problem and they provide statistical guarantees for the decision. These algorithms work poorly when the number of options are large, which unfortunately is the case in many practical applications. Multi-armed bandit methods are adaptive allocation rules designed for maximizing a predefined reward function that depends on the allocation during the experiment. Although bandit algorithms are shown to be optimal for the reward maximization, they cannot provide statistical confidence for the decision they make. Best-arm identification algorithms are adaptive exploratory allocation rules that achieve a compelling performance in finding the winner but they also fail to provide statistical guarantees. In this paper, we introduce OptoAllocate Confidence Interval (OACI), an adaptive allocation rule that assigns samples among options such that the confidence interval of the winning option has the largest separation from all other options’ confidence intervals. Simulations show that in many scenarios, OACI finds the winner of the test with a higher accuracy and with a better statistical guarantees than the variants of hypothesis testing, multi-armed bandit, or best-arm identification algorithms.

SDN-Based Smart Gateways (Sm-GWs) for Multi-Operator Small Cell Network Management

Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. However, current backhaul networks statically allocate resources, resulting in minuscule allocations when many small cells are connected to cellular operators with given resources. We introduce a novel access backhaul network architecture based on a smart gateway (Sm-GW) between the small cell base stations, e.g., long term evolution (LTE) enhanced Nodes B (eNBs), and the conventional backhaul gateways, e.g., LTE servicing/packet gateways (S/P-GWs). We specify the modest LTE protocol modifications that integrate the Sm-GW into the conventional LTE network. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Our simulation evaluations indicate that the Sm-GW scheduling can fairly allocate uplink transmission bitrates to the eNBs and reduce packet delays. Based on software defined networking (SDN), we introduce a management mechanism that allows multiple operators, i.e., multiple S/P-GWs, to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells. An SDN orchestrator coordinates the adaptive allocation of uplink transmission bitrates to Sm-GWs (which in turn allocate the uplink transmission bitrates to eNBs based on their demands). We formulate optimization problems for the operator (S/P-GW) resource allocation to Sm-GWs without and with sharing among operators. Our numerical evaluations indicate that the flexible SDN orchestration substantially increases the network throughput compared to the current static resource allocations.

A novel approach for cognitive radio application in 2.4-GHz ISM band

ABSTRACTThis paper reveals the issues of incorporating the cognitive radio (CR) technology in the 2.4-GHz industrial, scientific and medical band. The objective of allowing the coexisting systems in this unlicensed band is to opportunistically share the underutilised spectrum so as to improve the spectral usages efficiency. Hence, proper evaluation of the spectrum occupancy is one of the important tasks. Therefore, we adopt a double threshold-based detection scheme to differentiate the sub-bands with respect to their occupancy statistics satisfying the target miss detection probability. Further, an adaptive power allocation to the CR user (CRU) is proposed for maximising the system throughput under the constraints of interference to the co-existing systems and maximum transmission power. We consider path loss in the channel modelling between the CRUs. Our proposed approach is investigated over the real measurement data collected in the Swearingen Engineering Center, University of South Carolina, Columbia, SC, USA.

Adaptive Bit Allocation for 3D Video Coding

In the multi-view video plus depth 3D video coding, texture image and depth map are coded jointly. The texture image is utilized for displaying and synthesizing the virtual view as reference image. The depth map provides the scene geometry information and is utilized to synthesize the virtual view at the terminal through Depth-Image Based Rendering technique. The distortion of the compressed texture image and depth map will be propagated to the synthesized virtual view. Besides the coding efficiency of texture image and depth map, bit allocation between texture image and depth map also has a great effect on the synthesized virtual view quality. Several methods are proposed for bit allocation between texture image and depth map, but most of them attempt to allocate a fixed target bitrate based on virtual view distortion model to achieve optimal synthesized virtual view quality, and the modeling process brings extra complexity. In practical application, the video sequence has different contents and fixed bit ratio cannot achieve optimal performance. In this paper, we propose an adaptive bit allocation algorithm for 3D video coding. First, we present a model to estimate the synthesized virtual view distortion, and then adjust the bit ratio between adjacent views and between texture image and depth map at Group of Picture level based on the virtual view quality fluctuation. We adjust the bit ratio to achieve the optimal virtual view quality for different video contents. Experimental results demonstrate that the proposed algorithm can optimally allocate bits to achieve optimal virtual view quality under different target bitrates and for different video contents, and the computational complexity of the proposed algorithm is extremely low.

Interference Bound for Local Channel Allocation

A lower bound of the total co-channel interference is proposed for the channel allocation problem when applied to a reduced set of nodes. The rest of the network nodes remain unaffected. This bound is independent of the particular channel allocation algorithm employed and no assumptions are made about the propagation model or the deployment scenario. Assuming that the bound is tight to the interference generated by the optimal channel allocation, its computation may help, for example, to estimate the minimum set of nodes for which channel allocation performs nearly-optimal while minimizing node reconfigurations. Another example of usage is the estimation of the minimum number of channels required for a given performance. The tightness of the proposed bound is evaluated through simulations, with a difference lower than 1 % in the conducted simulations. In addition, a sample use case--adaptive local channel allocation--is also provided.

Energy Efficiency Optimization for Uplink Multiuser MIMO Systems

To satisfy green communication needs, energy efficiency is regraded as one of the most important indicators to evaluate the effectiveness of a communication system. In this paper, we establish the energy efficiency optimization framework for uplink multiuser mutliple-input multiple-output system. Taking the circuit power, the antenna link power and signal processing power into consideration, an analytical expression of the achieveable energy efficiency is firstly established, which can better model the practical communication process. Furthermore, we simplify the objective function under some mild conditions. After demonstrating the existence of a unique golbally optimal energy efficiency, an adaptive water-filling power allocation algorithm is proposed to further improve the energey efficiency of the system. Finally some simulation results are given to verify the performance of our scheme. Our work can provide a fundamental principles for uplink mutiluser multiple-input multiple-output systems design.

Intrinsically motivated particle swarm optimisation applied to task allocation for workplace hazard detection

This paper presents a framework for integrating intrinsic motivation with particle swarm optimisation. Intrinsically motivated particle swarm optimisation can be used for adaptive task allocation when the nature of the target task is not well understood in advance, or can change over time. We first present a general framework in which a computational model of motivation generates a dynamic fitness function to focus the attention of the particle swarm. We then discuss two approaches to modelling motivation in this framework: a computational model of curiosity using an unsupervised neural network and a model of novelty based on background subtraction. We introduce metrics for evaluating intrinsically motivated particle swarm optimisation and test our algorithm as an approach to task allocation in a workplace hazard mitigation scenario. We found that both proposed motivation techniques work well for generating a fitness function that can locate hazards, without requiring a precise definition of a hazard. We found that particle swarm optimisation can converge on optima in our generated fitness landscape in some, but not all, of our simulations.

Resource limitation and responses to rivals in males of the fruit fly Drosophila melanogaster.

Diet has a profound direct and indirect effect on reproductive success in both sexes. Variation in diet quality and quantity can significantly alter the capacity of females to lay eggs and of males to deliver courtship. Here, we tested the effect of dietary resource limitation on the ability of male Drosophila melanogaster to respond adaptively to rivals by extending their mating duration. Previous work carried out under ad libitum diet conditions showed that males exposed to rivals prior to mating significantly extend mating duration, transfer more ejaculate proteins and achieve higher reproductive success. Such adaptive responses are predicted to occur because male ejaculate production may be limited. Hence, ejaculate resources require allocation across different reproductive bouts, to balance current vs. future reproductive success. However, when males suffer dietary limitation, and potentially have fewer reproductive resources to apportion, we expect adaptive allocation of responses to rivals to be minimized. We tested this prediction and found that males held on agar‐only diets for 5–7 days lost the ability to extend mating following exposure to rivals. Interestingly, extended mating was retained in males held on low yeast/sugar: no sugar/yeast diet treatments, but was mostly lost when males were maintained on ‘imbalanced’ diets in which there was high yeast: no sugar and vice versa. Overall, the results show that males exhibit adaptive responses to rivals according to the degree of dietary resource limitation and to the ratio of individual diet components.

Adaptive energy-efficient power allocation for DAS with imperfect channel state information and antenna selection

Considering that perfect channel state information (CSI) is difficult to obtain in practice, energy efficiency (EE) for distributed antenna systems (DAS) based on imperfect CSI and antennas selection is investigated in Rayleigh fading channel. A novel EE that is defined as the average transmission rate divided by the total consumed power is introduced. In accordance with this definition, an adaptive power allocation (PA) scheme for DAS is proposed to maximize the EE under the maximum transmit power constraint. The solution of PA in the constrained EE optimization does exist and is unique. A practical iterative algorithm with Newton method is presented to obtain the solution of PA. The proposed scheme includes the one under perfect CSI as a special case, and it only needs large scale and statistical information. As a result, the scheme has low overhead and good robustness. The theoretical EE is also derived for performance evaluation, and simulation result shows the validity of the theoretical analysis. Moreover, EE can be enhanced by decreasing the estimation error and/ or path loss exponents.

Interference alignment with random vector quantisation in device‐to‐device underlaying cellular networks

In this study, the authors focus on the problem of interference alignment (IA) with random vector quantisation in device-to-device (D2D) uplink underlaying cellular networks. For a D2D underlaying system with one cellular network and one D2D local network, they first analyse the leakage interference introduced by limited feedback and, hence, imperfect IA. Then, they derive the exact closed-form expressions of average sum rate in terms of transmit power for cellular communication and D2D communication. Under such condition, they investigate the adaptive feedback bits allocation schemes to achieve near-optimal performance of systems with limited feedback including a greedy feedback bits allocation scheme and a waterfilling-based feedback bits allocation scheme. Finally, simulation results validate their theoretical results and show that significant performance gain can be obtained by allocating feedback bits adaptively.

Power allocation and subcarrier assignment for joint delivery of unicast and broadcast transmissions in OFDM systems

Most existing studies on broadcast services in orthogonal frequency division multiplexing (OFDM) systems have focused on how to allocate the transmission power to the subcarriers. However, because a broadcasting system must guarantee quality of service to all users, the performance of the broadcast service dominantly depends on the channel state of the user who has the lowest received signal-to-noise ratio among users. To reduce the effect of the worst user on the system performance, we propose a joint delivery scheme of unicast and broadcast transmissions in OFDM systems with broadcast and unicast best-effort users. In the proposed joint delivery scheme, the BS delivers the broadcast information using both the broadcast and unicast subcarriers at the same time in order to improve the performance of the broadcast service. The object of the proposed scheme is to minimize the outage probability of the broadcast service while maximizing the sum-rate of besteffort users. For the proposed joint delivery scheme, we develop an adaptive power and subcarrier allocation algorithm under the constraint of total transmission power. This paper shows that the optimal power allocation over each subcarrier in the proposed scheme has a multi-level water filling form. Because the power allocation and the subcarrier assignment problems should be jointly solved, we develop an iterative algorithm to find the optimal solution. Numerical results show that the proposed joint delivery scheme with adaptive power and subcarrier allocation outperforms the conventional scheme in terms of the outage probability of the broadcast service and the sum-rate of best-effort users.

Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems

ABSTRACTIn cognitive radio (CR) systems, reasonable power allocation can increase transmission rate of CR users or secondary users (SUs) as much as possible and at the same time insure normal communication among primary users (PUs). This study proposes an optimal power allocation scheme for the OFDM-based CR system with one SU influenced by multiple PU interference constraints. This scheme is based on an improved artificial fish swarm (IAFS) algorithm in combination with the advantage of conventional artificial fish swarm (ASF) algorithm and particle swarm optimisation (PSO) algorithm. In performance comparison of IAFS algorithm with other intelligent algorithms by simulations, the superiority of the IAFS algorithm is illustrated; this superiority results in better performance of our proposed scheme than that of the power allocation algorithms proposed by the previous studies in the same scenario. Furthermore, our proposed scheme can obtain higher transmission data rate under the multiple PU interference constraints and the total power constraint of SU than that of the other mentioned works.

Optimal and lead-in adaptive allocation for binary outcomes: A comparison of Bayesian methods

ABSTRACTWe compare posterior and predictive estimators and probabilities in response-adaptive randomization designs for two- and three-group clinical trials with binary outcomes. Adaptation based upon posterior estimates are discussed, as are two predictive probability algorithms: one using the traditional definition, the other using a skeptical distribution. Optimal and natural lead-in designs are covered. Simulation studies show that efficacy comparisons lead to more adaptation than center comparisons, though at some power loss, skeptically predictive efficacy comparisons and natural lead-in approaches lead to less adaptation but offer reduced allocation variability. Though nuanced, these results help clarify the power-adaptation trade-off in adaptive randomization.

Applying machine learning techniques for scaling out data quality algorithms in cloud computing environments

Deduplication is the task of identifying the entities in a data set which refer to the same real world object. Over the last decades, this problem has been largely investigated and many techniques have been proposed to improve the efficiency and effectiveness of the deduplication algorithms. As data sets become larger, such algorithms may generate critical bottlenecks regarding memory usage and execution time. In this context, cloud computing environments have been used for scaling out data quality algorithms. In this paper, we investigate the efficacy of different machine learning techniques for scaling out virtual clusters for the execution of deduplication algorithms under predefined time restrictions. We also propose specific heuristics (Best Performing Allocation, Probabilistic Best Performing Allocation, Tunable Allocation, Adaptive Allocation and Sliced Training Data) which, together with the machine learning techniques, are able to tune the virtual cluster estimations as demands fluctuate over time. The experiments we have carried out using multiple scale data sets have provided many insights regarding the adequacy of the considered machine learning algorithms and proposed heuristics for tackling cloud computing provisioning.

Distributed Learning-Based Cross-Layer Technique for Energy-Efficient Multicarrier Dynamic Spectrum Access With Adaptive Power Allocation

This paper proposes energy and cross-layer aware resource allocation techniques that allow dynamic spectrum access (DSA) users, by means of learning algorithms, to locate and exploit unused spectrum opportunities effectively. Specifically, we design private objective functions for DSA users with multiple channel access and adaptive power allocation capabilities. We also propose simple two-phase heuristics for allocating spectrum and power resources among users. The proposed heuristics split the spectrum and power allocation problem into two subproblems, and solve each of them separately. The spectrum allocation problem is solved, during the first phase using learning. Two procedures to learn the channel selection are proposed and compared in terms of optimality, scalability, and robustness. The power allocation, on the other hand, is formulated as a real optimization problem and solved, during the second phase, by traditional optimization solvers. Simulation results show that energy and cross-layer awareness and multiple channel access capability improve the performance of the system in terms of the per-user average rewards received from accessing the dynamic spectrum access system. In addition, the two proposed methods for channel selection via learning represent a tradeoff between optimality, scalability, and robustness.

Performance study of opportunistic scheduling in dual‐hop multi‐user underlay cognitive network

In this study, the authors investigate the performance of opportunistic scheduling for the dual-hop amplify-and-forward multi-user cognitive relaying network. Expressions are derived for the cumulative distribution function (CDF) and probability density function of the equivalent signal-to-noise ratio (SNR). From the derived CDF, the outage performance of the cognitive network is investigated. Then, an expression for average error probability is derived. Furthermore, simple and generic asymptotic expressions for the outage and error probabilities are obtained and discussed. In addition, a closed-form expression for the system's ergodic capacity is derived. Their asymptotic results show that opportunistic scheduling has no impact on diversity gain. It is confirmed that the array gain determines the SNR advantage of opportunistic scheduling over the single-user scenario. Moreover, they study adaptive power allocation under the total transmit power constraint in order to minimise the average error probability. As expected, the results show that optimum power allocation improves system performance compared with uniform power allocation. Finally, numerical results and Monte Carlo simulations are also provided to support the correctness of the analytical calculations.

An Efficient Parallel Krylov-Schur Method for Eigen-Analysis of Large-Scale Power Systems

An efficient parallel Krylov-Schur method is proposed for computing eigenvalues and eigenvectors related with oscillating modes of low damping for large-scale power systems. Improved restarting techniques are explained and demonstrated in details, which focus on a refined selection of kept subspace in contraction and a reliable mechanism of no missing target eigenvalues. Cayley and shift-invert transforms are used to decouple the computation of eigen-analysis and enable the proposed parallelization with a master-slave scheme. Based on the improved restarting techniques, the strategy for adaptive allocation of shifts and the coordination of parallel computing tasks, the proposed method is capable of computing a large number of eigen-pairs with satisfactory accuracy, convergence rate and parallel acceleration. The computational efficiency is validated by three test cases from real-world large-scale power systems on a symmetric multi-processing (SMP) computer.