Resource Allocation Problem Research Articles

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

A study of jamming resource allocation based on a hyperheuristic framework

There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. Above all, this study designs a fuzzy multiattribute evaluation model based on behavioral characteristics. The model comprehensively integrates the characteristics of signal behavior, motion behavior, and external intelligence and can effectively deal with unknown threat signals. Then, this study proposes an improved genetic selection electronic warfare operator (EWO) hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the HH framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four EWOs based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction, and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed.

Traffic-Aware Energy-Efficient Resource Allocation for RSMA Based UAV Communications

Traffic measurement will play an essential role in future networks to reveal the traffic requirements of the users, which will support network operations like resource allocation. In this paper, we study the traffic-aware resource allocation problem for downlink rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) communications. Specifically, with the help of traffic measurement, user requirements on achievable rate are known as prior knowledge to the UAV. Considering user requirements, we maximize the energy efficiency of the UAV by jointly optimizing the UAV deployment, beamforming, rate allocation, and subcarrier allocation. To overcome the non-convexity of the above problem, a joint optimization is developed to solve it iteratively. First, a heuristic approach is proposed to find the three-dimensional location of the UAV. Then, successive convex approximation approach is utilized to optimize RSMA parameters. Moreover, we formulate the subcarrier allocation problem as a many-to-one two-sided matching game, which is tackled by the swap matching algorithm. Numerical results suggest that the proposed subcarrier allocation scheme outperforms benchmark schemes, and RSMA based schemes perform close to that based on non-orthogonal multiple access, which all outperform orthogonal frequency division multiple access based scheme.

Distributed Strategies for Constrained Nonsmooth Resource Allocation Problems With Autonomous Second-Order Agents

Distributed Strategies for Constrained Nonsmooth Resource Allocation Problems With Autonomous Second-Order Agents

A Joint Communication and Computation Design for Probabilistic Semantic Communications

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme.

Online dynamic multi-user computation offloading and resource allocation for HAP-assisted MEC: an energy efficient approach

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

Resource Allocation and Pricing in Energy Harvesting Serverless Computing Internet of Things Networks

This paper considers a resource allocation problem involving servers and mobile users (MUs) operating in a serverless edge computing (SEC)-enabled Internet of Things (IoT) network. Each MU has a fixed budget, and each server is powered by the grid and has energy harvesting (EH) capability. Our objective is to maximize the revenue of the operator that operates the said servers and the number of resources purchased by the MUs. We propose a Stackelberg game approach, where servers and MUs act as leaders and followers, respectively. We prove the existence of a Stackelberg game equilibrium and develop an iterative algorithm to determine the final game equilibrium price. Simulation results show that the proposed scheme is efficient in terms of the SEC’s profit and MU’s demand. Moreover, both MUs and SECs gain benefits from renewable energy.

A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks

The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher’s perspective. Additionally, it is essential for a framework to be easily extendable with new types of problems and optimization algorithms, metrics and quality indicators, genetic operators or specific solution representations and results analysis and comparison features. After presenting the most relevant existing features in these types of platforms, we suggest some future steps and the developments we have been working on.

A route network resource allocation method based on multi-objective optimization and improved genetic algorithm

In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling.

Cost-efficient RAN slicing for service provisioning in 5G/B5G

Network slicing represents a substantial technological advance in 5G mobile network, greatly expanding the variety and manifoldness of network services to be supported. Additionally, 3GPP 5G New Radio (NR) has introduced novel features such as mixed numerology and mini-slots, which can be harnessed by network slicing to cater to the diverse requirements of 5G services. While however the co-existence of multiple network slices leads to a challenging resource allocation problem, these new features also severely complicate the management of radio resources. As a further point of attention, the virtualization of radio functions may exact a significant toll from the, already limited, computing resources at the network edge. It follows that a cost-efficient resource allocation across all the slices becomes crucial. In this paper, we address the above-mentioned issues by modeling a cost-efficient radio resource management in 5G NR featuring network slicing, named CERS, through a Mixed Integer Quadratically constrained Program (MIQCP). We maximize the profit of all slices simultaneously guaranteeing the target data rate and delay specified in the service level agreements (SLAs) fo the different traffic flows. To reduce the complexity of the MIQCP problem, we decompose it into two sub-problems, namely, the scheduling problem of enhanced Mobile Broadband (eMBB) user equipments (UEs) on a time-slot basis and of Ultra-Reliable Low Latency Communications (uRLLC) UEs on a mini-slot basis, while keeping the objective unchanged. To address the scheduling issue of eMBB UEs, we employ a heuristic technique, and, by leveraging the outcome of this heuristic, we derive an optimal solution for the problem of uRLLC UEs. The significance of the proposed approach over a baseline approach is evaluated through extensive numerical simulations in terms of the number of allocated uRLLC resource blocks (RBs) per mini-slot. We also assess our approach by measuring the impact of the uRLLC slice changes on the eMBB slice, and vice versa, including delay for uRLLC users and data rates for eMBB users.

Resource allocation in paired users: Optimization‐assisted user grouping for fairness improvement of NOMA

SummaryThe solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation.

A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush–Kuhn–Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.

A collective neurodynamic approach to distributed resource allocation with event-triggered communication

AbstractTo solve a distributed optimal resource allocation problem, a collective neurodynamic approach based on recurrent neural networks (RNNs) is proposed in this paper. Multiple RNNs cooperatively solve a global constrained optimization problem in which the objective function is a total of local non-smooth convex functions and is subject to local convex sets and a global equality constraint. Different from the projection dynamics to deal with local convex sets in the existing work, an internal dynamics with projection output is designed in the algorithm to relax the Slater’s condition satisfied by the optimal solution. To overcome continuous-time communication in a group of RNNs, an aperiodic communication scheme, called the event-triggered scheme, is presented to alleviate communication burden. It is analyzed that the convergence of the designed collective neurodynamic approach based on the event-triggered communication does not rely on global information. Furthermore, it is proved the freeness of the Zeno behavior in the event-triggered scheme. Two examples are presented to illustrate the obtained results

Robust Offloading for Edge Computing-Assisted Sensing and Communication Systems: A Deep Reinforcement Learning Approach.

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

A Fuzzy Inference Model to Identify the Current Industry Maturity Stage in the Transformation Process to Industry 4.0

The fourth industrial revolution has been developed and promoted unlike any other. In this regard, the measurement of achievements on every Industry IX.0 is subject to two major sources of imprecision, namely ambiguity and vagueness. In this paper, we introduce an Industry X.0 Fuzzy Inference Engine that, for a given industrial plant, it computes the technological footprint on Industries I1.0–I4.0; i.e., how much technology of each Industry IX.0 is present in that plant, therefore revealing its current maturity level in the transformation process to become an Industry 4.0. The model is scalable, and implements a control strategy inspired in resource allocation problems. It addresses the overlap issues common to I3.0 and I4.0, and assigns each criterion with a distinct importance. Our work stands out for bringing formal analytical methods supported by a theoretical background. To date, no work in the field has ever faced this problem as approached in the paper. Simulation results applied to hypothetical plants show the consistency and effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The need for leveraging all of the technologies currently available has made industrial plants to retrofit their facilities towards Industry 4.0. In order to properly assess the current stage of this transformation process, in real-world heterogeneous plants, we should measure the implementation extent of each automation technology, while considering where they fit in the industrial timeline. Existing models overlook important features of automated systems, such as control architecture and communication protocols, that among others determine whether a plant qualifies for one or more IX.0. Thus, in this paper we propose a Fuzzy inference engine that allows a thorough assessment and treatment of information, in order to state the current maturity stage (technological footprint) of a given plant with respect to Industries I1.0–4.0. The matrices are scalable, i.e., various plants may be evaluated all together for comparative purposes, the knowledge base can be improved, and other future IX.0 may be incorporated. The use of Fuzzy set and Fuzzy logic provided a suitable framework for modeling, quantifying, and reasoning under imprecise and unclear development stages of a transition for I4.0.

Optimal resources allocation to support the consensus reaching in group decision making

In group decision making (GDM), the minimum cost consensus model (MCCM) to assist a group to reach a consensus with the minimum cost has gained widespread attention. However, determining the unit costs for adjusting decision makers’ opinions in the MCCM is a challenging problem that limits its practical applications. Meanwhile, the MCCM is not modeled as a resources allocation problem in an explicit manner, and the opinions in the MCCM do not represent utilities/satisfactions, leading to the unclear implications of opinions’ adjustments. To overcome these limitations of the MCCM, this paper proposes the optimal resources allocation consensus model (ORACM) to assist the moderator to allocate resources without determining unit costs to support consensus reaching, through the introduction of the resources allocation problem and utility functions in its modeling. Furthermore, we present a theoretical analysis framework to reveal the properties of the ORACM and the connection between the ORACM and the MCCM, justifying the theoretical advantages of the ORACM. Moreover, the ORACM is applied to the transboundary river pollution control negotiations of Sichuan's Tuojiang River, and the effectiveness and feasibility of the ORACM are further validated with detailed simulation and comparison analyses.

Distributed Time-Varying Optimization-An Output Regulation Approach.

This article deals with distributed algorithm design for time-varying optimization problems, which include unconstrained time-varying optimization and a special constrained problem commonly known as a resource allocation problem. The time-varying nature exists in the individual cost functions and the demand functions, and they are then captured by neutrally stable linear dynamic systems known as exosystems. To address the time-varying nature, new distributed algorithm structures are developed and two algorithms are designed for distributed time-varying optimization (DTVO) and distributed time-varying optimal resource allocation (DTVORA) to ensure that there exist time-varying solutions and the solution states will converge to the time-varying solutions. The driving terms for tracking the variation in the solutions are designed using exosystem dynamics. Rigorous convergence analyses are carried out using Lyapunov theory, and the examples are included to demonstrate the potential applications of the two proposed algorithms.

Distributed Event-Triggered Algorithm Designs for Resource Allocation Problems via a Universal Scalar Function-Based Analysis.

In this article, we are concerned with distributed algorithm designs for resource allocation problems via event-triggered communication. The target is to search an optimal resource allocation scheme such that the summation of objective functions is minimized. Due to communication efficiency and privacy concerns, distributed algorithms with event-triggered communications are proposed in this article. The communication is only permitted or triggered if variation of gradient of the local objective function exceeds a threshold. By constructing a novel technical lemma and a universal scalar function, the convergence and linear convergence rates are established under some mild assumptions. Extensive numerical experiments on the IEEE 118-bus power system demonstrate that: Compared to the periodic algorithms, such as ADMM and Mirror-P-EXTRA, the proposed algorithms not only remarkably reduce the communication times but also have competitive convergence speed. The latter is striking that it implies there exist useless communications in the periodic algorithms that are censored by the proposed event-triggered strategy.