User Resource Allocation Research Articles

Joint Traffic Admission, Resource Allocation and Mode Selection Protocol for NOMA-Based D2D Users Underlaying Cellular Network for 5G and Beyond Networks

Joint Traffic Admission, Resource Allocation and Mode Selection Protocol for NOMA-Based D2D Users Underlaying Cellular Network for 5G and Beyond Networks

Sustainable soil management: Soil knowledge use and gaps in Europe

AbstractSoils are the foundation of agricultural production, ecosystem functioning and human well‐being. Bridging soil knowledge gaps and improving the knowledge system is crucial to meet the growing EU soil policy ambitions in the face of climate change and the ongoing trend in soil degradation. The objective of this article is to assess the current state of knowledge, knowledge use and knowledge gaps concerning sustainable soil management in Europe. This study is based on interviews with 791 stakeholders and 254 researchers and on a comprehensive review of >1800 documents carried out under the European Joint Programme on agricultural soils. Despite differences in stakeholder groups, the conclusions are rather consistent and complementary. We identified major knowledge gaps with respect to (1) soil carbon stocks, (2) soil degradation and fertility and (3) strategies for improved soil management. Transcending these three areas, particularly the loss of soil organic carbon, peatland degradation and soil compaction, are most critical, thus, we stress the urgency of developing more models and monitoring programmes on soils. Stakeholders further report that insufficient transfer of existing soil research findings to practitioners is a hindrance to the adoption of sustainable soil management practices. In addition to knowledge production, soil knowledge gaps may be addressed by considering seven recommendations from the stakeholders: (1) raising awareness, (2) strengthening knowledge brokers, (3) improving relevance of research activities and resource allocation for land users, (4) peer‐to‐peer communication, (5) targeting advice and information, (6) improving knowledge access, and (7) providing incentives. We argue that filling and bridging knowledge gaps should be a priority for policymakers and the insights provided in the article may help prioritise research and dissemination needs enabling a transition to more sustainable soil management in Europe.

Location Privacy-Aware Offloading for MEC-Enabled IoT: Optimality and Heuristics

With the rapid development of 5G and the Internet of Things (IoT), cloud-based access mode is applied more and more in IoT scenarios. The emerging IoT applications put forward higher requirements on energy consumption and processing capacity of the network, as well as more strict privacy restrictions. The emergence of mobile edge computing solves the aforementioned user experience problems, which greatly improves execution efficiency and saves a lot of energy. However, existing studies pay more attention to issues such as energy consumption and delay, but ignore the privacy and imbalanced server load problems that mobile edge computing may cause. This paper made bandwidth resource allocation and task offloading decisions for multiple users via joint optimization, provided the privacy requirements of IoT terminals and load balancing requirements are satisfied. The objective was to minimize the total energy consumption of all terminal devices. By modeling the problem as an integer linear programming problem, the optimal solution is obtained. Considering the high computational complexity of solving integer linear programming problems, a more practical heuristic algorithm was proposed. The simulation results showed the effectiveness of the proposed optimal and heuristic schemes in reducing energy consumption, and demonstrated the importance of privacy protection.

Energy efficiency resource allocation for cell-edge users with social-aware based grouping D2D

Energy efficiency resource allocation for cell-edge users with social-aware based grouping D2D

Federated Reinforcement Learning-Based Resource Allocation in D2D-Enabled 6G

The current 5G and conceived 6G era with ultrahigh density, ultra-high frequency bandwidth, and ultra-low latency can support emerging applications like Extended Reality (XR), Vehicle to Everything (V2X), and massive Internet of Things (IoT). With the rapid growth of transmission rate requirements and link numbers in the wireless communication network, how to allocate resources reasonably and further improve spectrum utilization challenges the traditional approaches. To address these problems, technologies such as device-to-device (D2D) communication and machine learning (ML) are introduced to the traditional cellular communication network to improve network performance. However, due to the interference caused by spectrum reusing, efficient resource allocation for both cellular users and D2D users is necessary. In this article, we consider underlay mode D2D-enabled wireless network to improve the spectrum utilization, and deep reinforcement learning (DRL)- based federated learning (FL)-aided decentralized resource allocation approach to maximize the sum capacity and minimize the overall power consumption while guaranteeing the quality of service (QoS) requirement of both cellular users and D2D users. The performance of the proposed schemes is evaluated through simulations under 5G millimeter-wave (mm-wave) and 6G terahertz (THz) scenarios separately. The simulation results show that the proposal achieves significant network performance compared with the baseline algorithms.

Dynamic User Resource Allocation for Downlink Multicarrier NOMA with an Actor–Critic Method

Future wireless communication systems require higher performance requirements. Based on this, we study the combinatorial optimization problem of power allocation and dynamic user pairing in a downlink multicarrier non-orthogonal multiple-access (NOMA) system scenario, aiming at maximizing the user sum rate of the overall system. Due to the complex coupling of variables, it is difficult and time-consuming to obtain an optimal solution, making engineering impractical. To circumvent the difficulties and obtain a sub-optimal solution, we decompose this optimization problem into two sub-problems. First, a closed-form expression for the optimal power allocation scheme is obtained for a given subchannel allocation. Then, we provide the optimal user-pairing scheme using the actor–critic (AC) algorithm. As a promising approach to solving the exhaustive problem, deep-reinforcement learning (DRL) possesses higher learning ability and better self-adaptive capability than traditional optimization methods. Simulation results have demonstrated that our method has significant advantages over traditional methods and other deep-learning algorithms, and effectively improves the communication performance of NOMA transmission to some extent.

Improve Throughput and spectrum efficiency using Cell – Free MIMO-NOMA network with user–centric clustering

In this article, the user resource allocation and user scheduling in a cell-free user-centric clustering network and coherent transmissions is investigated. However, the setup of the user-centric clustering is not sufficient for multi-user transmissions, and user scheduling becomes difficult. To overcome the aforementioned problem and to provide user fairness, an optimum resource allocation algorithm with perfect user scheduling, beam forming, and radio frequency transmitter (RFT) clustering is proposed. Thereby, the cell edge user achieves the optimum throughput and spectrum efficiency. The simulation result shows that the proposed method outperforms the conventional method. In addition, the pilot-reusing factor is used to obtain loss due to imperfect channel state information and pilot train overhead.

5G Multi-Slices Bi-Level Resource Allocation by Reinforcement Learning

As the centralized unit (CU)—distributed unit (DU) separation in the fifth generation mobile network (5G), the multi-slice and multi-scenario, can be better applied in wireless communication. The development of the 5G network to vertical industries makes its resource allocation also have an obvious hierarchical structure. In this paper, we propose a bi-level resource allocation model. The up-level objective in this model refers to the profit of the 5G operator through the base station allocating resources to slices. The lower-level objective in this model refers to the slices allocating the resource to its users fairly. The resource allocation problem is a complex optimization problem with mixed-discrete variables, so whether a resource allocation algorithm can quickly and accurately give the resource allocation scheme is the key to its practical application. According to the characteristics of the problem, we select the multi-agent twin delayed deep deterministic policy gradient (MATD3) to solve the upper slice resource allocation and the discrete and continuous twin delayed deep deterministic policy gradient (DCTD3) to solve the lower user resource allocation. It is crucial to accurately characterize the state, environment, and reward of reinforcement learning for solving practical problems. Thus, we provide an effective definition of the environment, state, action, and reward of MATD3 and DCTD3 for solving the bi-level resource allocation problem. We conduct some simulation experiments and compare it with the multi-agent deep deterministic policy gradient (MADDPG) algorithm and nested bi-level evolutionary algorithm (NBLEA). The experimental results show that the proposed algorithm can quickly provide a better resource allocation scheme.

Digital twin-assisted knowledge distillation framework for heterogeneous federated learning

In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset. To overcome the challenge of train the big teacher model in resource limited user devices, the digital twin (DT) is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources. Then, during model distillation, each user can update the parameters of its model at either the physical entity or the digital agent. The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming (MIP) problem. To solve the problem, Q-learning and optimization are jointly used, where Q-learning selects models for users and determines whether to train locally or on the server, and optimization is used to allocate resources for users based on the output of Q-learning. Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.

A DRL-Driven Intelligent Joint Optimization Strategy for Computation Offloading and Resource Allocation in Ubiquitous Edge IoT Systems

Intelligent computation offloading and resource allocation for mobile users (MUs) in ubiquitous edge Internet of Things (IoT) systems is a worthy research hotspot. To improve the latency and energy consumption of MUs in ubiquitous edge IoT systems, we propose a deep reinforcement learnin (DRL)-driven intelligent joint optimization strategy for computation offloading and resource allocation that includes relay selection, offloading decisions, and resource allocation. Specifically, according to the limited coverage of base stations and the extremely high deployment costs in actual environments, we introduce the virtual backbone architecture to provide users with efficient multi-hop offload services through a connected dominating set (CDS). Then, we propose a CDS-based deep reinforcement learning algorithm to search for the shortest path from the MUs to the multi-access edge computing (MEC) server. Furthermore, based on the highly coupled relationship between the offloading decision and resource allocation, we design a DLIO algorithm to solve for the joint optimization of computation offloading and resource allocation. The experimental results demonstrate that our proposed optimization algorithm outperforms the state-of-the-art methods in terms of total system cost, success rate, and acceptance number.

Joint Mode Selection and Resource Allocation for D2D and Femtocell Users in Dense Heterogeneous Networks with Full Frequency Reuse

We consider the problem of joint mode selection and resource allocation for D2D and femtocell users in a three-tier dense heterogeneous network in which all the users can reuse the spectrum. The goal is to maximize their total weighted sum rate, subject to minimum rate requirements and maximum tolerable interference on the cellular system. Since the spectrum can be fully reused, the co-tier and cross-tier interferences among the users result in a mixed integer non-linear, non-convex program that is difficult to solve directly. Using insights into the structure of the interference, we derive a close, conservative approximation of the joint problem that has a convex relaxation that is tight. That enables good solutions to the joint problem to be obtained using a customized iterative algorithm employing the Lagrange dual decomposition method. Since the proposed iterative scheme is semi-distributed, the signaling overhead is mitigated. To further reduce the computational complexity, a low-complexity (primal) decomposition-based method is also introduced, in which we select the transmission mode heuristically based on the traffic level in the network, and then sequentially perform admission control, power control, and sub-channel allocation for the femtocell users and D2D users. Our simulation results indicate that the proposed iterative and heuristic algorithms, on average, achieve around 94% and 82% of the sum rate of the optimal Branch & Bound method, respectively, and do so at much lower computational costs.

Weighted De-Synchronization Based Resource Allocation in Wireless Networks

Considering the exponential growth of wireless devices with data-starving applications fused with artificial intelligence, the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment. The Kuramoto model is described as nonlinear self-sustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength, in which a mutual behavior is accomplished. In this work, we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network, where each user has different quality of service (QoS) requirements. Because the original Kuramoto model is the synchronization model, we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieve weighted fair resource allocation for users with different QoS requirements. The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner. We analyze the convergence condition for the proposed model, and the results reveal that the proposed algorithm achieves a weighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.

Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning

Cloud computing leads to efficient resource allocation for network users. In order to achieve efficient allocation, many research activities have been conducted so far. Some researchers focus on classical optimisation theory techniques (such as multi-objective optimisation, evolutionary optimisation, game theory, etc.) to satisfy network providers and network users’ service-level agreement (SLA) requirements. Normally, in a cloud data centre network (CDCN), it is difficult to jointly satisfy both the cloud provider and cloud customer’ utilities, and this leads to complex combinatorial problems, which are usually NP-hard. Recently, machine learning and artificial intelligence techniques have received much attention from the networking community because of their capability to solve complicated networking problems. In the current work, at first, the holistic utility satisfaction for the cloud data centre provider and customers is formulated as a reinforcement learning (RL) problem with a specific reward function, which is a convex summation of users’ utility functions and cloud provider’s utility. The user utility functions are modelled as a function of cloud virtualised resources (such as storage, CPU, RAM), connection bandwidth, and also, the network-based expected packet loss and round-trip time factors associated with the cloud users. The cloud provider utility function is modelled as a function of resource prices and energy dissipation costs. Afterwards, a Q-learning implementation of the mentioned RL algorithm is introduced, which is able to converge to the optimal solution in an online and fast manner. The simulation results exhibit the enhanced convergence speed and computational complexity properties of the proposed method in comparison with similar approaches from the joint cloud customer/provider utility satisfaction perspective. To evaluate the scalability property of the proposed method, the results are also repeated for different cloud user population scenarios (small, medium, and large).

Resource Allocation for D2D Communication in Multiservice Cellular Network

Allowing different services with different requirements to coexist is a hot topic for future communication. In order to meet the different communication requirements of users, this paper proposes a cellular communication system based on filtered orthogonal frequency division multiplexing (filtered-OFDM) supporting device-to-device (D2D) communication. Further, in order to improve the system performance, we study the user resource allocation problem in the system and propose a dynamic iterative resource allocation algorithm based on Hungarian algorithm (DRABH). The algorithm allocates resource blocks for D2D user pairs and all cellular users in the cell under the constraint of the lowest threshold of signal to interference plus noise ratio (SINR), and cellular users have high priority. Under the condition of ensuring that all cellular users can communicate, resource blocks are allocated to as many D2D user pairs as possible to maximize the number of user links in the network. Simulation results show that considering the interference introduced by filtered-OFDM and D2D communication, the proposed resource allocation scheme can significantly improve the number of user links.

User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks

Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.

Hybrid multi-user access scheme for a visible light communication system.

A hybrid multi-user access scheme based on orthogonal frequency-division multiple access and non-orthogonal multiple access (OFDMA-NOMA) is proposed for visible light communication (VLC) systems with a bandwidth-constrained channel. In the scheme, a novel user resource allocation method is used, to the best of our knowledge, to reduce the unfairness among different users caused by the high-frequency fading characteristic of VLC systems. Furthermore, the particle swarm optimization (PSO) is introduced to satisfy the transmitting power requirements of each user and to improve system performance. Simulation results show that, under the same VLC system conditions, the OFDMA-NOMA scheme with PSO has significant improvements in terms of user capacity and transmission rates compared to the purely NOMA scheme and the OFDMA scheme.

Cellular Network Power Allocation Algorithm Based on Deep Reinforcement Learning and Artificial Intelligence.

In the shortest path planning problem, the old algorithm usually has many defects, such as the robot's cognition being contrary to reality, the lack of practical operation feasibility, or the limitation of problem processing. Nowadays, with deep learning, artificial intelligence algorithms tend to be mature; it has become a mainstream trend to adopt end-to-end learning system instead of traditional old algorithms. In recent years, with the rise of the Internet of things emerging technology industry and the explosive surge of network data traffic, the drawback is the increasingly severe shortage of wireless spectrum resources. In order to effectively reduce the cochannel interference of D2D communication technology in the system and enhance the useable range of the cellular network, it is necessary to distribute the useful and efficient cellular resources of the system. In this article, we will study the D2D users and the selection scheme of D2D users' transmission power control mode and allocate the spectrum resources in the uplink of the cellular users in the communication network. In order to reduce the cochannel interference in a cellular network and improve the spectrum utilization of the system, the research direction of this article is to solve the problem of user communication resource allocation in a single-cell hybrid cellular network.

Joint User Scheduling and Resource Allocation in Distributed MIMO Systems with Multi-Carriers

Compared with the traditional collocated multi-input multi-output system (C-MIMO), distributed MIMO (D-MIMO) systems have the advantage of higher throughput and coverage, making them strong candidates for next-generation communication architecture. As a practical implementation of a D-MIMO cooperative network, the multi-TRP (multiple transmission/reception point) system becomes a hotspot in the research of advanced 5G. Different from previous research on a cooperative D-MIMO network with single narrowband transmission, this paper proposes a joint optimization scheme to address the user scheduling problem along with carrier allocation to maximize the total spectral efficiency (SE) in the downlink of coherent multi-TRP systems with multi-carriers. We establish a joint optimization model of user scheduling and resource allocation to maximize the system spectral efficiency under the constraints of power consumption and the backhaul capacity limits at each RAU (remote antenna unit), as well as the QoS (quality of service) requirement at each user. Since the optimization model is both non-covex and non-smooth, a joint optimization algorithm is proposed to solve this non-convex combinatorial optimization problem. We first smooth the mixed-integer problem by employing penalty functions, and after decoupling the coupled variables by introducing auxiliary variables, the original problem is transformed into a series of tractable convex optimization problems by using successive convex approximation (SCA). Numerical results demonstrate that the proposed joint optimization algorithm for user scheduling and resource allocation can reliably converge and achieve a higher system SE than the general multi-TRP system without carrier allocation, and this advantage is more pronounced under a higher backhaul capacity or higher power consumption constraints.

A Comprehensive Analysis on Secured Data Storage with User Validation and Resource Allocation in Cloud for Performance Enhancement

A Comprehensive Analysis on Secured Data Storage with User Validation and Resource Allocation in Cloud for Performance Enhancement

A Novel Approach to the Resource Allocation for the Cell Edge Users in 5G

In 5G network, resource allocation for the cell edge users is the major challenge. To address this challenge, we present GFDM (Generalized Frequency Division Multiplexing) for the physical layer of 5G wireless networks is a non orthogonal waveform with circularly pulse shaped mechanism. This mechanism is also used for resource allocation. In this paper, to allocate the weights on the filter bank of GFDM for cell edge users, an optimized Deep Neural Network (DNN) is presented in this paper. To enhance the performance of the DNN, weight parameters of it are optimized using Rain Optimization Algorithm (ROA). Using this proposed ROA based DNN, weight resources are allocated to the cell edge users optimally. Simulation results shows that the performance of the proposed resource allocation outperforms the conventional resource allocation in terms of normalized cell throughput.