Network Allocation Research Articles

Joint Beam Direction Control and Radio Resource Allocation in Dynamic Multi-Beam LEO Satellite Networks

Joint Beam Direction Control and Radio Resource Allocation in Dynamic Multi-Beam LEO Satellite Networks

DNN-based algorithm for joint SIC ordering and power allocation in downlink NOMA-enabled heterogeneous networks

In the heterogeneous network (HetNet) employing downlink non-orthogonal multiple access (NOMA), we focus on the non-convex optimization problem to optimize the spectral efficiency (SE) while the users satisfy the quality-of-service (QoS) requirement. In the previous work, the optimal joint successive interference cancellation and power allocation (JSPA) algorithm for maximizing SE is proposed to solve the mixed-integer non-linear programming (MINLP) problem in NOMA-enabled HetNet. However, the optimal solution requires exponential complexity by the number of base stations (BSs). Therefore, we present a deep neural network (DNN)-based algorithm for JSPA to reduce the complexity. In particular, to deal with the MINLP-based JSPA problem, we reformulate it into an equivalently simple problem that optimizes only the power consumption of BSs. Then, we introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance compared with traditional DNN-based methods. Additionally, we propose a user selection scheme with low complexity to enhance the SE of the proposed DNN-based power allocation. Through simulations, we illustrate that the suggested DNN-based scheme can attain SE performance similar to that of the optimal scheme.

Spectrum-Energy-Efficient Mode Selection and Resource Allocation for Heterogeneous V2X Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

Spectrum-Energy-Efficient Mode Selection and Resource Allocation for Heterogeneous V2X Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

Decentralized Routing and Radio Resource Allocation in Wireless Ad Hoc Networks via Graph Reinforcement Learning

Decentralized Routing and Radio Resource Allocation in Wireless Ad Hoc Networks via Graph Reinforcement Learning

Multi-armed bandit approach for mean field game-based resource allocation in NOMA networks

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process.

Planning and resource allocation of a hybrid IoT network using artificial intelligence

This paper introduces a pioneering hybrid topology tailored for Internet of Things (IoT) applications, integrating mesh and star wireless sensor configurations. This hybridized approach aims to optimize energy consumption efficiency while ensuring comprehensive network coverage for sensor deployment. The formulation of the network strategy is rooted in empirical data collected from real sensors deployed across two neighboring municipalities within the State of Espírito Santo, Brazil. Specifically, our analysis encompasses 380 strategically positioned sensors throughout Vitória city, all intended for connectivity to a central gateway located in Vila Velha. To establish mesh network clusters, we employed the k-Medoids algorithm for clustering fusion, while the GA with a binary solution was utilized to determine the star network points. In this approach, Dijkstra and genetic algorithms with real solutions are incorporated to facilitate efficient resource allocation within the mesh (utilizing ZigBee) and star (utilizing LoRa) networks. These resource allocation strategies are devised with the overarching objective of minimizing energy consumption. The findings of this investigation demonstrate that through the implementation of planning and resource allocation algorithms, we were able to effectively reduce the number of mesh networks and allocate resources to each designated end-point sensor.

Sequential multichannel allocation in cache-enabled multicasting small-cell networks

This paper investigates the optimization of multichannel allocation in cache-enabled multicasting small-cell networks (CMSNs). In the traditional fixed channel allocation scheme (FCA), small-cell base stations (SBSs) allocate a fix channel to each local cache popular content. In order to further increasing the utilization of allocated channels, we first propose a sequential multichannel allocation scheme (SMA), in which SBSs allocate a sequential channel set to their cached files dynamically. We formulate the problem of sequential multichannel allocation as two NP-hard non-linear programming problems with two optimization objectives of minimizing the global collision level (GCL) and maximizing the global file-downloading level (GFL), and demonstrate the advantages of the proposed DSCA over FCA. Then, two channel allocation potential games are proposed to achieve GCL and GFL optimization respectively. To achieve Nash equilibria (NEs) of the proposed games, we present a better response based SMA algorithm (BR-SMA) for both two games, and propose an improved better response based SMA algorithm (IBR-SMA) for the game for GCL maximization which have two exploration modes for each decision-maker.

Optimizing resource allocation in UAV-assisted ultra-dense networks for enhanced performance and security

The deployment of unmanned aerial vehicles (UAVs) in ultra-dense networks (UNDs) has significantly advanced network capabilities in 5G/6G environments, addressing coverage enhancement and security concerns. Our research presents a deep reinforcement learning (DRL) based approach designed to manage the increasing data traffic demands and limited communication resources in UAV-assisted UNDs. Traditional DRL methodologies often struggle with challenges like low sample efficiency and energy wastage, which can indirectly impact network security and stability. To address these concerns, we introduce the Stabilizing Transformers based Potential Driven Reinforcement Learning (STPD-RL) framework. STPD-RL optimizes critical network operations such as transmission link selection and power allocation, directly contributing to improved energy efficiency and robust network performance. Initially, we have refined the potential driven experience replay and implemented it into resource allocation in UAV-assisted UDN for the inaugural time. By assigning a potential energy function to each state in experience replay, users can employ intrinsic state supervision to learn from a spectrum of good and bad experiences. Subsequently, we have employed stabilizing transformers to hasten the learning trajectory for resource allocation policies, thereby enhancing the stability of model training. Furthermore, we have integrated potential driven experience replay and stabilizing transformers within the Proximal Policy Optimization algorithm, thus formulating our uniquely tailored STPD-PPO. In simulations with many users and base stations, STPD-PPO outperformed traditional PPO in metrics such as entropy loss, policy loss, and value loss. Results suggest that our STPD-PPO surpasses traditional DRL algorithms in several respects, including convergence rate, energy efficiency, total power consumption, and exploration capacity.

Resource allocation in multi-user cellular networks: A transformer-based deep reinforcement learning approach

Resource allocation in multi-user cellular networks: A transformer-based deep reinforcement learning approach

Variable Hybrid Action Space Deep Q-Networks for Optimal Power Allocation and User Association in Heterogeneous Networks

Variable Hybrid Action Space Deep Q-Networks for Optimal Power Allocation and User Association in Heterogeneous Networks

Cost-Aware Computation Offloading and Resource Allocation in Ultra-Dense Multi-Cell, Multi-User and Multi-Task MEC Networks

Cost-Aware Computation Offloading and Resource Allocation in Ultra-Dense Multi-Cell, Multi-User and Multi-Task MEC Networks

Joint Resource Allocation for RIS-Assisted Heterogeneous Networks With Centralized and Distributed Frameworks

Joint Resource Allocation for RIS-Assisted Heterogeneous Networks With Centralized and Distributed Frameworks

Enhancing Performance of Power Allocation for VLC Networks by Non-Orthogonal Multiple Access-MIMO

Visible light communication, or VLC, networks emerged as a viable option for data access, particularly indoors. Their cost-effectiveness, protection to radio frequency (RF) intrusion, and extraordinarily high data speeds make them a desirable option for the upcoming generation of indoor networking technologies. In this paper, we propose the Exponential Gain Ratio Power Allocation (EGRPA), an effective and low-complex power splitting method, to increase the attainable sum amount in Multiple Input Multiple Output (MIMO) VLC downlink networks. Using numerical simulations, we assess the enactment of an indoor 2x2 MIMO VLC downlink model for several users. We also use Non-Orthogonal Multiple Access (NOMA) for enhancing spectrum efficacy. The usefulness and resilience of the suggested power splitting approach are established by the simulation outcomes, especially in scenarios with a high user count. In particular, the EGRPA power allocation algorithm shows a sum rate enhancement of more than 38.7%, 7.86%, and 7.04% for a system with three users when compared to the GRPA method, Normalized Gain Difference Power Allocation (NGDPA) method, and Logarithmic GRPA (NLGRPA) method, correspondingly.

Standing tall like Caesar? Qatar’s unwavering voice for Palestine at the United Nations

This article examines the complexities of Qatar’s Palestine policy, emphasising Doha’s multifaceted role as a mediator, problem-solver, humanitarian aid provider, and representative of the Palestinian issue at the United Nations. The study employs a mixed-methods research approach. Firstly, it utilises qualitative data from official statements, key policymakers’ remarks, and literature on Qatar’s foreign policy and Palestinian engagement. Secondly, it introduces text analytic methods such as latent Dirichlet allocation (LDA) topic modelling, word clouds and network analysis to identify key themes, relationships and trends in Qatar’s speeches. The findings reveal that the Palestinian issue has been a prominent and consistent topic in Qatar’s United Nations General Assembly General Debate addresses over the past five decades. Qatar has consistently advocated for a resolution to the conflict, emphasising the importance of peace, justice and the two-state solution. Qatar’s support for Palestine remains steadfast, despite pressure from other regional actors to end its engagement with Hamas and divert its attention from the Palestinian issue. This commitment to the Palestinian cause aligns with Qatar’s broader state preferences for an independent foreign policy, its opposition to external interference, and its aspiration to restore its role as a trusted international partner on global platforms.

Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

The Spectrum is a scarce resource in wireless networks, hence these resources need to be perfectly channelized for their better utilization. By the study of the previous decades, it is found that the spectrum is colossally underutilized and the main reason for the underutilization found out to be the policies that are fixed and not dynamic. The dynamic spectrum allocation of frequency bands may overcome this problem. Cognitive radio provides an important concept that can used to solve the problem of underutilization of spectrum. Reinforcement learning is a key technique that is widely used to learn the spectrum allocation behaviour and maximize the system’s efficiency. Therefore, in this work, we have designed and developed a Reinforcement learning-based model to allocate the channels among secondary users. Also, a Deep Reinforcement learning-based channel allocation algorithm (DRLCA) has been proposed. The proposed DRLCA is compared with existing JPCRL [1]. In our algorithm, the Python libraries were used for simulation. From the simulation results and analysis, it is found that the DRLCA outperforms the JPCRL in terms of channel utilization by 5%.

Energy Consumption Forecasting through Data Mining

Forecasting is vital for effective energy management systems to project future energy requirements and establish demand-supply equilibrium. In this study, we propose to evaluate the effectiveness of a new weather-free forecasting model. The model is created using advanced data mining techniques on an extensive database that contains relevant historical power production data. The motivation behind developing a weather-free model stems from the challenges associated with obtaining reliable weather data in specific scenarios and the desire to reduce computational complexity. By eliminating the reliance on weather data, our model offers a promising alternative approach to energy forecasting, potentially enhancing accuracy and efficiency. In this work, our objectives are twofold. Firstly, we aim to evaluate the interplay between anomaly detection techniques and forecasting model accuracy. Anomalies in energy consumption patterns can significantly impact forecasting accuracy, and thus, understanding their detection and management is crucial. Secondly, we seek to determine the optimal performance metric among three defined metrics explicitly tailored for this application. By addressing these objectives, we aim to contribute to advancing energy forecasting methodologies, providing valuable insights for practical implementation in energy management systems. Our findings have the potential to inform decision-making processes and optimize resource allocation in energy distribution networks, ultimately leading to more sustainable and resilient energy infrastructures.

Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention.

A key question in the neuroscience of memory encoding pertains to the mechanisms by which afferent stimuli are allocated within memory networks. This issue is especially pronounced in the domain of working memory, where capacity is finite. Presumably the brain must embed some "policy" by which to allocate these mnemonic resources in an online manner in order to maximally represent and store afferent information for as long as possible and without interference from subsequent stimuli. Here, we engage this question through a top-down theoretical modeling framework. We formally optimize a gating mechanism that projects afferent stimuli onto a finite number of memory slots within a recurrent network architecture. In the absence of external input, the activity in each slot attenuates over time (i.e., a process of gradual forgetting). It turns out that the optimal gating policy consists of a direct projection from sensory activity to memory slots, alongside an activity-dependent lateral inhibition. Interestingly, allocating resources myopically (greedily with respect to the current stimulus) leads to efficient utilization of slots over time. In other words, later-arriving stimuli are distributed across slots in such a way that the network state is minimally shifted and so prior signals are minimally "overwritten." Further, networks with heterogeneity in the timescales of their forgetting rates retain stimuli better than those that are more homogeneous. Our results suggest how online, recurrent networks working on temporally localized objectives without high-level supervision can nonetheless implement efficient allocation of memory resources over time.

Mitigating Security Attacks in Spectrum Allocation in Cognitive Radio Networks

Mitigating Security Attacks in Spectrum Allocation in Cognitive Radio Networks

Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service

ABSTRACT The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptability and scalability in complex, dynamically-changing environments. This paper introduces the Dynamic Resource Allocator using RL-CNN (DRARLCNN), a novel machine learning model addressing these shortcomings. By merging Convolutional Neural Networks (CNN) for feature extraction and Reinforcement Learning (RL) for decision-making, DRARLCNN optimizes resource allocation, minimizing latency and maximizing Quality of Service (QoS). Utilizing a state-of-the-art “5G Resource Allocation Dataset”, the research employs Python, TensorFlow, and OpenAI Gym to implement and test the model in a simulated 5 G environment. Results demonstrate the effectiveness of DRARLCNN, showcasing an impressive R 2 score of 0.517, MSE of 0.035, and RMSE of 0.188, surpassing existing methods in allocation efficiency and latency. The DRARLCNN model not only outperforms existing methods in allocation efficiency and latency but also sets a new benchmark for future research in dynamic 5G resource allocation. Through its innovative approach and promising results, DRARLCNN opens avenues for further advancements in optimizing resource allocation within dynamic 5G networks.

Study of distribution network loss allocation with high photovoltaic penetration ratio

With the rapid development of renewable energy, the penetration ratio of photovoltaic (PV) in the distribution network is getting higher and higher. The fair and reasonable allocation of high loss caused by high PV penetration in the distribution network is of great significance to the healthy development of distribution systems. In order to study the loss allocation in distribution networks with high PV penetration, based on the cooperative game theory, combined with the power flow algorithm, the power flow and loss of the 72-node active distribution network in agricultural and pastoral areas were analyzed. Three scenarios were included: no PV, low PV penetration, and high PV penetration. The allocated loss of the distribution network with high PV penetration and multiple distributed generators was computed. The results show that the loss allocation method, which takes into account the power as well as the network topology, is applicable to distribution networks with multiple distributed generators. In distribution networks with high PV penetration, distributed generators are the main cause of high loss. If the network contains the extreme reverse power flow concerned in this paper, the loss allocated to DGs accounts for more than 95% of the total network power loss.