Optimal Allocation Problem Research Articles

Mitigating Jamming Attack in 5G Heterogeneous Networks: A Federated Deep Reinforcement Learning Approach

Jamming attack is one of the serious security breaches in the upcoming fifth-generation heterogeneous networks (5G HetNets). Most of the existing anti-jamming techniques, such as frequency hopping (FH) and direct sequence spread spectrum (DSSS) lack in self-adaptive capabilities to mitigate the security and privacy issues in highly dynamic 5G HetNet environment. In literature, although reinforcement learning (RL) has been explored a lot in designing various anti-jamming techniques to address the aforementioned problems, but these techniques suffer from the issue of large network resource consumption and slow convergence rate. To mitigate these issues, we propose a federated deep reinforcement learning (DRL) based anti-jamming technique for two-tier 5G HetNets. In the proposal, each femtocell of 5G HetNets is assumed to have multiple single antenna femto users (FUs) and a multi-antenna jammer used to jam the downlink signals from femto base station (FBS) to FUs. Aiming to improve the achievable rate at FUs in the presence of jammers, a joint optimization problem of beamforming and power allocation at FBSs is formulated by considering the quality-of-service (QoS) requirements of FUs. Due to the non-convex nature of the aforementioned optimization problem, we have used the Markov decision process (MDP) to transform the optimization problem into a multi-agent reinforcement learning (MARL) problem. Then, to solve this MDP with large number of states and action spaces, a federated deep reinforcement learning (DRL) scheme is proposed to maximize the achievable rate at FUs. The proposed scheme uses federated learning and dueling architecture of dueling double deep Q network (D3QN) to optimize the beamforming vectors and power allocation jointly at FBSs. The achievable rate performance of the proposed federated DRL scheme is compared with double deep Q network (DDQN) and deep Q network (DQN). Simulation results show that the proposed federated DRL scheme achieves 19.39% and 23.85% better achievable rate in comparison to DDQN and DQN schemes.

Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.

Integrated Sensing and Communication Aided Dynamic Resource Allocation for Random Access in Satellite Terrestrial Relay Networks

An integrated sensing and communication (ISAC) aided dynamic resource allocation is proposed for random access (RA) in Internet-of-Things (IoT) oriented satellite terrestrial relay networks (STRNs). Specifically, we design an ISAC aided RA architecture, which consists of sensing phase and RA phase. Relays can sense the number of active users by wireless sensing and assist RA with the sensed results. Active users are grouped into orthogonal clusters in the spectrum domain according to relays that they belong to. To appropriately utilize the bandwidth resources, a dynamic resource allocation optimization problem is formulated and solved, which is able to dynamically allocate bandwidth to each cluster according to the number of intra-cluster active users. Based on allocated bandwidth, active users can always perform optimal uplink RA, which maximizes the total system throughput. Simulation results show that the proposed scheme greatly outperforms the existing schemes in terms of total system throughput and packet loss rate (PLR).

Optimal allocation of EVs parking lots and DG in micro grid using two‐stage GA‐PSO

In this paper, the optimal allocation problem of the Electric Vehicles Parking Lots and DG in micro-grid is studied as a novel optimization problem. In the proposed problem, the different factors including the technical and the economic issues are considered for achieving a realistic solution. In addition, FACTS devices have been added to the network by considering the costs of installation, repair and maintenance, and their effecton the technical and economic parameters of the network has been investigated. Also, how to optimally charge and discharge storage devices and how to optimally inject active or reactive power by charging stations to improve energy management in the network has been studied. In order to evaluate the impact and reduce economic costs, the optimal number of distributed renewable energy, storage and FACTS equipment along with the optimal number of parking lots with Existence of different number of electric vehicles in the survey and microgrid network has been designed using a two-stage optimization method. The method is simulated on the standard IEEE 33 bus. The results show that the optimal power factor of parking lots can be determined during the unloading of vehicles in such a way that the technical parameter of the network is improved.

A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services.

Wireless body area network (WBAN) is widely adopted in healthcare services, providing remote real-time and continuous healthcare monitoring. With the massive increase of detective sensor data, WBAN is largely restricted by limited storage and computation capacity, resulting in severely decreased efficiency and reliability. Mobile edge computing (MEC) technique can be combined with WBAN to resolve this issue. This paper studies the joint optimization problem of computational offloading and resource allocation (JCORA) in MEC for healthcare service scenarios. We formulate JCORA as a Markov decision process and propose a deep deterministic policy gradient-based WBAN offloading strategy (DDPG-WOS) to optimize time delay and energy consumption in interfered transmission channels. This scheme employs MEC to mitigate the computation pressure on a single WBAN and increase the transmission ability. Further, DDPG-WOS optimizes the offloading strategy-making process by considering the channel condition, transmission quality, computation ability and energy consumption. Simulation results verify the effectiveness of the proposed optimization schema in reducing energy consumption and computation latency and increasing the utility of WBAN compared to two competitive solutions.

Optimal liquidity allocation in an equity network

We study an optimal liquidity allocation problem in a distressed financial system when agents are embedded in a network of cross-ownership. We construct a model in which a benevolent social planner allocates a limited amount of funds to rescue agents facing liquidity shortages and whose liquidation can be avoided if the shortage is met. A discrete allocation problem arises in this setting and the “Balas” algorithm is employed to solve this problem. Solving the optimal liquidity allocation problem over a class of core–periphery equity network topologies, we establish the existence of a threshold value of exposures levels between core and peripheral nodes above which the social planner would allocate available funds to the periphery and below which to the core. The algorithm demonstrates how monetary and fiscal authorities may implement rescue plans when the size of the fund is limited and could thus serve as a new intervention tool in the arsenal of policymakers.

Joint VNF Placement, CPU Allocation, and Flow Routing for Traffic Changes

The emerging network-softwarization technologies, such as software-defined networking and network function virtualization play important roles in 5G communication and future networks. One of the critical challenges of the practical application of the softwarized networks is to appropriately place virtual network functions (VNFs). The underlying resources and traffic requirements are often factored in the previous works of VNF placement. However, VNFs’ dynamic abilities of changing traffic are usually ignored. Resources allocated to VNFs can vary their traffic-change ratios, and the adjustment of resource volumes should be provisioned to support traffic changes. In this work, we pay attention to the joint optimization problem of VNF Placement, CPU Allocation, and flow Routing (VNFPAR) in the scenarios consisting of VNFs that can dynamically change traffic. We employ the logarithmic functions to approximate VNFs’ traffic change relations and formulate VNFPAR as a mixed-integer nonlinear programming (MINLP) problem. We demonstrate that this problem is highly nonconvex and involves highly coupled variables. For small-scale VNFPAR problems, we propose an optimal algorithm based on relaxation and programming to consume the minimum bandwidth resources. Because VNFPAR is NP-hard, to quickly find near-optimal solutions for large-scale VNFPAR problems, we present heuristic algorithms based on multistage greedy and simulated annealing, respectively. Besides, to achieve a tradeoff between solution quality and execution time, we decompose VNFPAR into subproblems and design an alternating optimization-based method. We evaluate our algorithms on real-work topologies and traffic patterns. Extensive simulations show that our proposed heuristic algorithms are convergent, stable, and effective in terms of solving VNFPARs. The proposed algorithms have small optimality gaps within 7.4%–26.3%. Meanwhile, they save 39.3%–48.4% bandwidth resources compared with relevant baseline technologies.

Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

AbstractAnalysis and optimization of system reliability have very much importance for developing an optimal design for the system while using the available resources. Several studies are centered towards reliability optimization using metaheuristics. In this study, a recently developed metaheuristic optimization algorithm called hybrid PSO‐GWO (HPSGWO) to solve the reliability‐redundancy optimization problem has been proposed. The HPSGWO fuses the Particle Swarm Optimization's (PSO) exploitation ability with the grey wolf optimizer's (GWO) exploration ability. The comparison of results with prior best results of PSO and GWO for the four benchmarks of reliability redundancy allocation problem demonstrates the HPSGWO as a productive enhancement strategy since it got promising answers than other metaheuristic algorithms.

A Multi-Objective Decision Model for Water Pollution Load Allocation under Uncertainty

In order to control the discharge of regional total pollutants in the region and improve the ability of water environment management and decision making, a multi-objective decision-making optimization model of water pollution load allocation was constructed, which took into account economy and fairness. The model takes the maximum environmental benefit and the minimum weighted comprehensive Gini coefficient as the objective function and takes into account the uncertainty and multi-objectives of the model, which is conducive to promoting economic development and ensuring the fairness of regional water pollutant discharge. A method based on Monte Carlo simulation coupled with a genetic algorithm was designed to obtain the optimal solution set through multiple simulation optimization. This model is applied to Anhui Province to solve the allocation optimization problem of total pollutant reduction in the 13th Five-Year Energy Conservation and Emission Reduction Plan. After the optimization of water pollution load distribution, the comprehensive Gini coefficients of COD and NH3-N are reduced by different ranges. The comprehensive Gini coefficient after COD optimization decreased by 2.4–4.6%, and the comprehensive Gini coefficient after NH3-N optimization decreased by 25.1–32.5%, which verified the feasibility and rationality of the model in the optimal allocation of the total discharge of regional water pollutants. The model takes into account uncertain subjective and objective factors that have an important impact on water pollutant discharge targets and decision variables, thus optimizing the total emissions of the entire regional control unit in both space and time.

Comparative assessment of techno-economic and environmental benefits in optimal allocation of distributed generators in distribution networks

Integration of Distributed Generation (DG) into power systems, especially at the distribution end, is one of the verified approaches that has been utilized to reduce power loss, enhanced reliable electricity supply, and promote environmental sustainability by reducing Greenhouse Gas (GHG) emissions. In this study, an approach for solving an optimal DG allocation problem in distribution network with the objective function of maximizing the financial Techno-Economic and Environmental Benefits (TEEBs) of the grid is discussed. The TEEBs analysis of the DG planning problem is uniquely modeled as financial cost-benefit due to reduced power purchased and reduced Penalty Emission Cost (PEC) arising from the reduction of GHG emission in the network. A comprehensive and comparative analysis was carried out for the four classes of DG technology types to identify the environmental impact of integrating renewable and non-renewable DGs into the distribution system. Furthermore, The DG planning problem is solved using the Black Widow Optimizer (BWO) technique. The study implemented the proposed methodology on the standard IEEE 69-bus and Nigerian Shasa 59-bus distribution systems. The results show that renewable DGs’ optimal integration yielded higher TEEBs than non-renewable DGs despite the high capital cost of renewable DGs. Furthermore, the study affirmed the viability and efficiency of the proposed method by comparing the results of power loss obtained for the various DG types with that of techniques in open works of literature.

Combination of Throughput-optimal Scheduling and Network Utility Maximization in NOMA Systems with Flow-Level Dynamics

In wireless networks, the network queue stability (throughput-optimality) and the network utility maximization are two crucial tasks for network operators. Besides, new techniques of mobile communications, e.g. the non-orthogonal multiple access (NOMA), have emerged endlessly to meet coming challenges. In this paper, we for the first time, investigate a downlink NOMA system with flow-level dynamics, where long-lived flows and short-lived flows exist simultaneously, and formulate an optimization problem of user selection and power allocation. We aim to maximize the network utility at each time slot and simultaneously guarantee the throughput-optimality, under the quality of service (QoS) constraints. Considering the practicability of algorithm, the suboptimal algorithm containing two stages, is proposed. And we analyze the complexity of proposed algorithm. In the performance evaluation, the benchmark algorithm is provided for comparison. And we take the energy efficiency (EE) as an example of the network utility and define the scheduling delay (SD). Simulation results show that the suboptimal algorithm is throughput-optimal, and understandably, its performances could be better than those in the benchmark algorithm in terms of EE and SD, as long as the appropriate parameter is set.

Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.

SecBoost: Secrecy-Aware Deep Reinforcement Learning Based Energy-Efficient Scheme for 5G HetNets

In this paper, we propose a secrecy-aware energy-efficient scheme for a two-tier heterogeneous network (HetNet), consisting of a sub-6 GHz macrocell and multiple millimeter wave (mmWave) picocells. Each picocell is assumed to have several users and an eavesdropper (Eve) which intercepts the signal of the picocell users. In the proposed scheme, firstly, to maximize the secrecy energy-efficiency (SEE) of picocell users, a joint optimization problem of power control, channel allocation, and beamforming is formulated by considering the minimum secrecy rate and signal-to-interference-plus-noise ratio (SINR) constraints. Due to the non-convex nature of the aforementioned optimization problem in a highly dynamic HetNet environment, we transform it into a reinforcement learning (RL) problem using the Markov decision process (MDP). Then, a multi-agent reinforcement learning (MARL) technique is used to obtain the maximum long-term reward. Moreover, we propose a multi-agent cooperative deep reinforcement learning (DRL) scheme known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> to solve the MDP with large number of action and state spaces. It uses the dueling and double-Q architecture of dueling double deep Q-network (D3QN) to optimize power control, channel allocation, and beamforming vectors to maximize the SEE of picocells. Also, prioritized experience replay is used to increase the sampling efficiency of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> . The SEE performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> is compared with MARL, multi-agent deep Q-network (MA-DQN), state-of-the-art joint beamforming based secrecy energy efficiency maximization (JBF-SEEM) scheme, and one-time pad based encrypted data transmission (O-EDT). Simulation results demonstrated that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> scheme achieves 14.7%, 8.33%, 30%, and 69% better average SEE in comparison to MARL, MA-DQN, JBF-SEEM, and O-EDT schemes, respectively, which reveals its effectiveness in improving SEE of picocells.

Optimal capital allocation for individual risk model using a Mean-Variance Principle

&lt;p style='text-indent:20px;'&gt;In this article, we study the problem of optimal capital allocation for the individual risk model using the Mean-Variance principle with quadratic loss function when a total amount of initial capital is granted for allocation. The determination of the optimal capital allocation strategy proceeds in two phases. First, explicit formulas for the optimal allocations are presented based on the assumption that the claim occurrence indicators are independent of the claim severities when the allocated total capital is fixed. Second, an approximating algorithm is proposed to find out the optimal value of capital used for allocation through minimizing the mean-variance loss function. As a result, the exact allocation policy can be provided by following the first phase again. Numerical examples and applications are provided to illustrate the main results and some discussions on the effect of neglecting the dependence between the claim occurrence indicators and claim severities on the optimal allocations are also given to shed light on future research.&lt;/p&gt;

Portfolio performance under benchmarking relative loss and portfolio insurance: From omega ratio to loss aversion

AbstractWe study an optimal investment problem under a joint limited expected relative loss and portfolio insurance constraint with a general random benchmark. By making use of a static Lagrangian method in a complete market setting, the optimal wealth and investment strategy can be fully determined along with the existence and uniqueness of the Lagrangian multipliers. Our numerical demonstration for various commonly used random benchmarks shows a trade-off between the portfolio outperformance and underperformance relative to the benchmark, which may not be captured by the widely used Omega ratio and its utility-transformed version, reflecting the impact of the benchmarking loss constraint. Furthermore, we develop a new portfolio performance measurement indicator that incorporates the agent’s utility loss aversion relative to the benchmark via solving an equivalent optimal asset allocation problem with a benchmark-reference-based preference. We show that the expected utility performance is well depicted by looking at this new portfolio performance ratio, suggesting a more suitable portfolio performance measurement under a limited loss constraint relative to a possibly random benchmark.

Trajectory Optimization and Power Allocation for Cell-Free Satellite-UAV Internet of Things

Massive access outside the coverage of terrestrial cellular networks will be the main feature of the sixth generation (6G) networks. In order to cope with it, the cognitive satellite-UAV network (CSUN) has drawn a lot of attentions. In this paper, we investigate the UAV trajectory optimization and power allocation for the cell-free CSUN consisting of one satellite and a swarm of UAVs. Indeed, due to the on-board energy constraints of UAVs, both the trajectory optimization and the power allocation can significantly save the energy to improve the energy efficiency. The joint trajectory optimization and power allocation problem is formulated as a mixed-integer non-convex optimization problem which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original optimization problem into two subproblems in terms of the trajectory optimization and power allocation. For the trajectory optimization subproblem, we model it as a Traveling Salesman Problem (TSP), and the PSO is adopted to solve it. When the trajectory variables are fixed, the power allocation subproblem is still difficult to tackle due to its non-convexity and large scale. Firstly, we present a kind of centralized algorithm in which the DC (difference of two convex functions) algorithm is applied to optimize it, and then a distributed algorithm based on auxiliary variables is proposed to reduce the signaling overhead and computational complexity. The simulation results demonstrate the effectiveness of the proposed joint trajectory optimization and power allocation algorithm for the cell-free CSUN.

Optimizing data locality by executor allocation in spark computing environment

Data locality is an important concept in big data processing. Most of the existing research optimized data locality from the aspect of task scheduling. However, as the execution container of tasks, the executors started on which nodes can directly affect the locality level achieved by the tasks. This paper tries to improve the data locality by executor allocation for reduce stage in Spark computing environment. Firstly, we calculate the network distance matrix of executors and formulate an optimal executor allocation problem to minimize the total communication distance. Then, when the network distance between executors satisfies the triangular inequality, an approximate algorithm is proposed; and when the network distance between executors does not satisfy the triangular inequality, a greedy algorithm is proposed. Finally, we evaluate the performance of our algorithms in a practical Spark cluster by using several representative micro-benchmarks (Sort and Join) and macro-benchmarks (PageRank and LDA). Experimental results show that the proposed algorithms can decrease the execution time of tasks for lower data communication.

Salp swarm algorithm applied to optimal capacitor allocation problem in distribution network for annual cost savings

Salp swarm algorithm applied to optimal capacitor allocation problem in distribution network for annual cost savings

State-dependent optimal incentive allocation protocols for cooperation in public goods games on regular networks

Incentives are often used to promote cooperation in a population of competing agents. Furthermore, it is a common method to combine both punishment and reward. However, how to optimally allocate an amount of incentive budget as reward and punishment to enhance cooperation in structured populations is still a challenging task. To address this problem, we here consider evolutionary public goods games on regular networks and assume that a fixed budget of incentives is dynamically allocated to reward cooperators or to punish defectors in a group of players, depending on the actual cooperation level in the population. By means of the pair approximation approach, we derive the dynamical equation for depicting the evolutionary dynamics of cooperation. We then formulate two optimal incentive allocation problems by minimizing the payoff difference between defectors and cooperators and maximizing the gradient of selection, respectively. We theoretically derive the optimal incentive allocation protocols for the two objective functions we considered. We find that the obtained protocols both depend sensitively on the efficiency ratio of reward to punishment. In addition, we provide numerical calculations to verify our theoretical results.

Robust optimal investment strategy for a DC pension plan in the market with mispricing and constant elasticity of variance

This paper considers an optimal asset allocation problem for a defined contribution pension fund in a continuous time setting, and the manager is concerned about potential model misspecification. We suppose that the financial market consists of one risk-free asset, one market index fund whose price satisfies the constant elasticity of variance model, and a pair of risky assets with mispricing. Under the objective of maximizing the expected utility of the pension fund wealth at the retirement, the closed form expressions of the robust optimal investment strategy and the corresponding value function are obtained by using the stochastic control theory. Finally, some numerical examples are given to compare the difference between the ambiguity averse and the ambiguity neutral strategies, and to investigate the effect of relevant parameters on the strategy. We find that the optimal investment strategy is not a long-short symmetric strategy when the mispricing assets have different mispricing correct capability, and it will lead to significant utility loss for the manager if the model uncertainty risk is ignored.