Optimal Allocation Problem Research Articles

Bio-inspired and artificial intelligence enabled hydro-economic model for diversified agricultural management

Neoteric phenomena such as climate change, scarce water availability and excessive fertilizer usage necessitate an augmentation of resource utilisation efficiencies in the agricultural sector. There is a need to reorient the agroecosystems to curb stress on environmental resources while meeting rising socio-economic objectives under changing hydro-climatic conditions. Considering this, optimal land allocation for diversified agriculture is essential. We propose a combinatorial optimisation approach for land allocation considering agronomic, socio-economic, environmental and hydro-climatic objectives using bio-inspired optimization algorithms. The stochastic approach tackles the problem of optimal agricultural land allocation for crops in a multidimensional context by simultaneously addressing the conflicting goals of farm-level risk management as well as district-level contingency planning. The efficiencies and sensitivity of the proposed framework are assessed through a case study of the Dharwad district in Karnataka, India using the data (water and fertilizer consumption and cost, crop type, cultivable land, man and machine hours, etc.) from the year 2019–2020. Results indicate that Multi-objective Genetic Algorithm (MOGA) is more capable of optimising agricultural resources management by suggesting optimal land allocation for diversified crop planning. Although Cuckoo Search (CS) and Particle Search Optimisation (PSO) also produced productive Pareto fronts, they were observed to be less effective than MOGA. The annual increase in profits and crop yield obtained using MOGA are 103% and 97% respectively, while water usage is reduced by 5% compared to the conventional routines in Dharwad. The proposed hydro-agronomic decision support framework (DSF) can be utilised to assist the AI-enabled crop planning process for the sustainable management of agroecosystems.

Proportional rate constrained resource allocation in multicarrier nonorthogonal multiple access systems

AbstractIn multicarrier nonorthogonal multiple access (MC‐NOMA) systems, resource allocation for user rate fairness is an inherent challenge due to the huge difference in channel conditions among different users. As the general case of proportional fairness, proportional rate constraint is usually adopted in resource allocation optimization problems to ensure that achievable rates of users are distributed proportionally according to a set of arbitrarily predefined portion. In this article, we investigate joint subcarrier and power allocation in MC‐NOMA systems under proportional rate constraints. The formulated resource allocation optimization problem belongs to a mixed integer nonlinear programming (MINLP) problem due to its binary subcarrier allocation variables and nonaffine equality constraints. By introducing an auxiliary variable in proportional rate constraints and substituting power variable with rate variable, we convert this nonconvex problem into a convex optimization problem and solve it by using Karush‐Kuhn‐Tucker (KKT) conditions and the dual decomposition method. Based on the optimal solutions, we divide the resource allocation process into two phases, that is, subcarrier allocation and power allocation, and develop an optimal subcarrier allocation solution based resource allocation algorithm (OSSRA). Furthermore, we also propose a heuristic algorithm, referred to as proportional rate constrained subcarrier allocation algorithm (PCSAA), as a benchmark. Numerical results show that, substantial gains can be achieved by OSSRA over the existing work and the traditional orthogonal multiple access (OMA) schemes in terms of both sum rate and user fairness. Besides, PCSAA has better user fairness performance than OSSRA.

Joint optimization of dynamic resource allocation and packet scheduling for virtual switches in cognitive internet of vehicles

The rapidly evolving machine learning technologies have reshaped the transportation system and played an essential role in the Cognitive Internet of Vehicles (CIoV). Most of the cognitive services are computation-intensive or storage-intensive, and thus they are usually deployed in edge or cloud data centers. In today’s data center networks, the virtual machines hosted in a server are connected to a virtual switch responsible for forwarding all packets for the cognitive services deployed on the virtual machines. Therefore, the virtual switches will become a performance bottleneck for cognitive services without an efficient resource allocation and data scheduling strategy. However, the highly dynamic characteristics of cognitive services make the resource allocation and packet scheduling problem for virtual switches surprisingly challenging. To guarantee the performance of cognitive services, we investigate the joint optimization problem of dynamic resource allocation and packet scheduling for virtual switches. We first model the joint optimization problem of dynamic resource allocation and packet scheduling for virtual switches as a mathematical optimization problem. Then, we analyze the problem with Lyapunov Optimization Framework and derive efficient optimization algorithms with performance tradeoff bounds. At last, we evaluate these algorithms on a testbed and a network-wide simulation platform. Experiment results show that our algorithms outperform other designs and meet the theoretical performance bound.

Green berth and yard space allocation under carbon tax policy in tidal ports

ABSTRACT The green and sustainable development of ports is an issue of great concern to governments, industries, and academia. Carbon emissions near ports mainly come from ships and trucks. The effective allocation of resources and the implementation of a carbon tax policy may reduce carbon emissions near ports. Therefore, this paper investigates an integrated optimization problem of berths, quay cranes, and yard spaces allocation for tidal ports with channel capacity constraints under carbon tax policy. A mathematical model that aims to minimize the sum of the total carbon emission costs and total penalty costs is proposed. To solve the model efficiently, a heuristic algorithm based on the sequential method is developed. Numerical experiments are performed to verify the efficiency of the proposed algorithm. Based on the results of numerical experiments, some potentially useful managerial implications are outlined.

Distributed Q-Learning-Enabled Multi-Dimensional Spectrum Sharing Security Scheme for 6G Wireless Communication

In 6G wireless communication scenarios, the ultra-dense network (UDN) scenario has received extensive attention, as it is an inevitable case of 6G networks. How to keep users safe while providing universal service to a number of users is becoming a crucial topic of UDN. To battle this issue, we propose a distributed Q-learning-enabled multi-dimensional spectrum sharing security scheme for the millimeter-wave frequency band. With this scheme, a fine-grained spectrum sharing security framework based on spatial multiplexing is established, which balances UDN performance and security. Aiming at the multi-dimensional spectrum sharing security and optimal allocation problem, non-cooperative game theory is used for modeling and analysis. To prove the validity of this scheme, we used numerical simulations to verify the performance of the proposed distributed Q-learning algorithm for solving the spectrum sharing optimization problem in the UDN scenario. The results show that the proposed multi-dimensional spectrum sharing security scheme can significantly reduce the access delay of users, increase the number of unauthorized users that can be accommodated, and improve throughput, while achieving good security performance of the network.

Effective Throughput Maximization of NOMA With Practical Modulations

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effective throughput</i> , which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes.

A Novel Hybrid Duplex Scheme for Relay Channel: Joint Optimization of Full-Duplex Duty Cycle and Source Power Allocation

Full-duplex (FD) mode has great potential in improving the spectral efficiency. Mitigating the effect of self-interference becomes one key for performance enhancement of FD system. This work proposes a novel hybrid duplex scheme where the relay receives information for a fraction of time and simultaneously transmits and receives information for the rest, following a duty cycle. First, we formulate the achievable rate maximization of the proposed scheme as a joint FD duty cycle and source power allocation optimization problem. The optimal FD duty cycle, the optimal source power allocation, and the maximal achievable rate are explicitly given for some cases and characterized in detail for other cases. Then, the proposed scheme is applied to two-hop relaying systems. Specifically, the optimal source power allocation is proved to be a water-filling solution over the FD phase and the receives-only phase on the source-relay link. By dividing the system as low-, medium-, and high-source power cases, the optimal FD duty cycle and the maximal achievable rate are obtained in (approximate) closed-form case-by-case, where the source power thresholds among cases are clearly expressed. Numerical results validate that the proposed hybrid duplex scheme outperforms other benchmark schemes and can improve the achievable rate significantly.

Enhancing the performance of downlink NOMA relaying networks by RF energy harvesting and data buffering at relay

Recently, non-orthogonal multiple access (NOMA) and energy harvesting (EH) from radio frequency (RF) have been considered as promising candidates for next-generation mobile communications. In this paper, we investigate a novel solution to enhance the reliability and the supply stability of a downlink NOMA relaying networks, in which we integrate two techniques: (i) simultaneous wireless information and power transfer, i.e. the relay node can harvest the energy from source signals and use this energy to help forward information from source node to two user nodes; and (ii) data buffer aid at relay node, i.e. the data packets received from the source can be stored in a buffer and then be re-transmitted to the destination nodes only when the channel condition is good. The performance of the proposed system is analyzed rigorously to derive the system outage probability (OP), throughput and the average packet delay. Furthermore, a power allocation optimization problem to minimize the OP is formulated and solution to this problem is also provided. In addition, the optimal transmission rates to maximize the throughput of each user are presented in numerical results. Monte Carlo simulations are conducted to verify the analytical results, which confirms that with data buffer at relay, the overall outage probability (OOP) can be reduced significantly.

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory.

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.

Cost-based analysis and optimization of distributed generations and shunt capacitors incorporated into distribution systems with nonlinear demand modeling

This paper optimizes the allocations of Distributed Generators (DG) and Shunt Capacitor (SC) banks to minimize a multi-objective problem consisting of four different indices. These indices include active/reactive power loss reduction, voltage profile improvement, and system stability enhancement. Consequently, the costs of energy loss, allocated devices, and total operation are estimated per year for different installed DG/SC pairs for studied systems. Multi-DG and SC banks are optimally installed in the standard 69-bus and 136-bus radial distribution systems considering six nonlinear load models. The six models are generated from the nonlinear relationship between the system voltage and demand. Furthermore, the load type affects the DG/SC allocation optimization problem and other performance parameters such as the total voltage deviation (TVD), stability index (SI), and the power drawn from the utility. A new metaheuristic optimization called the Jellyfish Search Algorithm (JSA) is adopted and applied to allocate the DG units and the SC banks optimally into the distribution systems. The savings in the annual energy loss reach 65.88%, 95.99%, and 97.91% with industrial load demand for 1DG/1SC, 2DG/2SC, and 3DG/3SC allocations, respectively for the 69-bus RDS. While for the 136-bus RDS, the saving reaches 39.71% with constant-impedance demand, 58.84%, and 73.87% with commercial demand for 1DG/1SC, 3DG/3SC, and 6DG/6SC pairs, respectively. It is found that the JSA is practical and suitable for solving these nonlinear optimization problems, and it gives better results as compared to other algorithms in the literature.

Deep Reinforcement Learning for Task Offloading and Power Allocation in UAV-Assisted MEC System

Mobile edge computing (MEC) can provide computing services for mobile users (MUs) by offloading computing tasks to edge clouds through wireless access networks. Unmanned aerial vehicles (UAVs) are deployed as supplementary edge clouds to provide effective MEC services for MUs with poor wireless communication condition. In this paper, a joint task offloading and power allocation (TOPA) optimization problem is investigated in UAV-assisted MEC system. Since the joint TOPA problem has a strong non-convex characteristic, a method based on deep reinforcement learning is proposed. Specifically, the joint TOPA problem is modeled as Markov decision process. Then, considering the large state space and continuous action space, a twin delayed deep deterministic policy gradient algorithm is proposed. Simulation results show that the proposed scheme has lower smoothing training cost than other optimization methods.

Optimization of Water Distribution Networks Using Genetic Algorithm Based SOP–WDN Program

Water distribution networks are vital hydraulic infrastructures, essential for providing consumers with sufficient water of appropriate quality. The cost of construction, operation, and maintenance of such networks is extremely large. The problem of optimization of a water distribution network is governed by the type of water distribution network and the size of pipelines placed in the distribution network. This problem of optimal diameter allocation of pipes in a distribution network has been heavily researched over the past few decades. This study describes the development of an algorithm, ‘Smart Optimization Program for Water Distribution Networks’ (SOP–WDN), which applies genetic algorithm to the problem of the least-cost design of water distribution networks. SOP–WDN demonstrates the application of an evolutionary optimization technique, i.e., genetic algorithm, linked with a hydraulic simulation solver EPANET, for the optimal design of water distribution networks. The developed algorithm was applied to three benchmark water distribution network optimization problems and produced consistently good results. SOP–WDN can be utilized as a tool for guiding engineers during the design and rehabilitation of water distribution pipelines.

Optimal carry trade portfolio choice under regime shifts

This paper studies optimal currency allocation of the carry trade in foreign exchange (FX). A number of empirical studies have documented a phenomenon referred to as the ‘forward premium puzzle’, which states that the carry trade is profitable on average. However, the carry reversal during the 2008 global crisis periods wiped out the profits earned by the trade. To account for the regime shifts in the joint distribution of returns on the carry trade with FX market portfolios, we adopt a Markov regime switching model. We find evidence of two economic regimes: one state captures periods of the forward premium puzzle and UIP being violated, whilst the other reports high FX volatility and negative return of carry trade portfolio. Furthermore, to quantify the economic significance of regimes in returns on currency portfolios, we consider their importance in investors’ optimal portfolio allocation problem. We find strong evidence that optimal currency portfolio holdings vary significantly across regimes and across short and long investment horizons, as investors anticipate a shift out of the current state. Our results also show that the regimes partially anticipate peso events as well as the changes of funding liquidity measured by the TED spreads. A strategy that accounts for regimes, thus, generates overall stronger performance than the single-state Gaussian IID model, a carry trade procedure as well as a buy-and-hold strategy.

Reliability analysis and optimal generator allocation and protection strategy of a non-repairable power grid system

In a power grid system, generators may fail due to the internal structure and material degradation caused by stress levels or work conditions. However, power grid systems may also be subject to external impacts such as landslides and earthquakes. Protecting these systems can mitigate the external negative effects. In this research, we extend the general network reliability model with consideration of the individual generator protection and the overarching protection. The generators can be destroyed if and only if both the individual protection and the overarching protection are destroyed. A joint generator allocation and protection optimization problem is formulated to increase the system reliability. We propose an algorithm to evaluate the system reliability by extending the universal generating function technique. Numerical experiments show that determining the generator allocation and the protection strategy optimally can improve the system reliability.

Dynamic programming approach for fuzzy linear programming problems FLPs and its application to optimal resource allocation problems in education system

This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable.

Resource Allocation for OFDM-NOMA Systems under Multiuser Scenarios with Different Speeds

In this paper, we consider an OFDM-NOMA (orthogonal frequency division multiplexing-nonorthogonal multiple access) downlink system, which has multiple subcarriers and multiple users at different locations and speeds from the base station. The goal is to maximize energy efficiency (EE), which is the bit rate per unit of energy consumption, while meeting the user’s quality of service and power constraints. Since the optimization problem of joint user subcarrier allocation and power allocation is a mixed-integer nonlinear programming (MINLP) problem, the use of traversal search will produce an unacceptable amount of calculation. It should be noted that the optimization problem includes the variables of user and subcarrier allocation and power allocation coefficient. In order to effectively solve this problem, we divide the problem into two subproblems: the problem of user and subcarrier allocation and the problem of power allocation between users. We use a matching algorithm to solve the problem of user and subcarrier assignment. An iterative algorithm to ensure convergence is proposed to obtain the power budget between subcarriers. Finally, the effective binary search is used to obtain the power allocation among users. The simulation results show that the proposed algorithm has a faster convergence rate. At the same time, as the number of users and subcarriers increases, EE improves significantly. As the user speed increases, the EE gradually decreases; especially when the number of users is larger, the effect is more obvious. Compared with random matching, the proposed algorithm has higher EE under the same number of users, number of subcarriers, and speed.

Multi-Agent Reinforcement Learning in NOMA-Aided UAV Networks for Cellular Offloading

A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. Since ground mobile users are considered as roaming continuously, the UAVs need to be re-deployed timely based on the movement of users. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional deep Q-network (DQN) algorithm, the MDQN algorithm enables the experience of multi-agent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm is capable of converging under minor constraints and has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than invoking the circular trajectory and the 2D trajectory, respectively.

Performance Analysis and Optimization of LDM-Based Layered Multicast

The explosion of mobile communication devices and diversification of emerging media content put forward higher requirements for mobile communication networks in various aspects such as overall throughput, fairness and user experience. Fortunately, non-orthogonal multiplexing (NOM) techniques such as layered-division-multiplexing (LDM) and the flexible compatibility of physical layer design in fifth generation mobile networks (5G) open up the possibility to tackle the challenges. In this paper, we propose to use LDM-based layered multicast to improve network performance, where the multicast content is delivered with differentiated quality in different LDM signal layers. For various performance optimization purposes such as maximum throughput, proportional fairness, minimum dissatisfaction index, and maximum service satisfaction index, a unified analysis framework is developed. Under this framework, we formulate a joint optimization problem of layer-user pairing and power allocation. In order to solve this problem, an optimization algorithm based on subproblem decomposition is proposed, which arranges each multicast subscriber of the multicast content to receive the most suitable LDM layer, and finds the optimal transmit power and data rate for each layer. Simulation results demonstrate the superiority of LDM-based layered multicast over existing multicast approaches while guaranteeing the coverage and the lower limit of user experience.

Enhanced User Grouping and Power Allocation for Hybrid mmWave MIMO-NOMA Systems

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider uplink communications with a hybrid beamforming structure and focus on improving the spectral efficiency (SE) and energy efficiency (EE) of mmWave multiple-input multiple-output (MIMO)-NOMA systems with enhanced user grouping and power allocation. It is noted that the optimization of the SE/EE is a challenging task due to the non-linear programming nature of the corresponding problem involving user grouping, beam selection, and power allocation. Our idea is to decompose the overall optimization problem into a mixed integer problem comprised of user grouping and beam selection only, followed by a continuous problem involving power allocation and digital beamforming design. Exploiting the directionality property of mmWave channels, we first propose a novel initial agglomerative nesting (AGNES) based user grouping algorithm by taking advantage of the channel correlations. To avoid the prohibitively high complexity of the brute-force search approach and to address the overlapping beam problem, we propose two suboptimal low-complexity user grouping and beam selection schemes, the two-stage direct AGNES (D-AGNES) scheme and the joint successive AGNES (S-AGNES) scheme. We also introduce the quadratic transform (QT) to recast the non-convex power allocation optimization problem into a convex one subject to a minimum required data rate of each user. The continuous problem is solved by iteratively optimizing the power and the digital beamforming. Extensive simulation results have shown that our proposed mmWave-NOMA design outperforms the conventional orthogonal multiple access (OMA) scenario and the state-of-art NOMA schemes.

Performance Analysis and Resource Allocation for a Relaying LoRa System Considering Random Nodal Distances

In conventional star-topology LoRa networks, the gateways are expected to collect the data from all the nodes nearby. However, a major challenge for the conventional LoRa system is the performance degradation due to the long-range communication over fading channels. To resolve the challenging issue, this paper investigates a two-hop amplify-and-forward relaying LoRa network in a two-dimension plane, where random nodal distances are considered. Moreover, a relay-selection mechanism is developed for the proposed system. Based on the best relay-selection protocol, the analytical bit-error-rate (BER) and asymptotic BER expressions, achievable diversity order, coverage probability, and throughput of the proposed system are derived over the Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel. Furthermore, to maximize the throughput of the proposed system, a two-dimensional resource allocation optimization problem (i.e., the spread factor selection and power allocation optimization) is formulated and investigated. The proposed optimal spread factor selection and power allocation scheme is verified to outperform the two baseline schemes. Simulation and numerical results show that although the proposed system reduces the throughput compared to the conventional LoRa system, it significantly improves the BER and coverage probability. Hence, the proposed system can be considered as a promising technique for low-power, long-range and highly reliable Internet-of-Things applications.