Resource Management In Wireless Networks Research Articles

ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

Research on Agricultural Enterprise Accounting Information Resource Sharing Model Based On Big Data Technology

The conventional enterprise accounting information resource sharing model has the problem of fuzzy accounting information characteristics, resulting in low schedulability. An agricultural enterprise accounting information resource sharing model based on big data technology is designed. Obtain the goal of agricultural enterprise business process reengineering, establish the standard process of mutual integration of enterprise accounting information, combined with the principle of data mining, extract the characteristics of accounting information by using big data technology, divide the spectrum module according to the deployment density of small cells, design the heterogeneous wireless network resource management model, optimize the resource distribution level, and construct the resource sharing model by using hierarchical scheduling algorithm. Experimental results: the mean schedulability rates of the enterprise accounting information resource sharing model and the other two models are 60.073%, 49.801% and 49.319%, which proves that the enterprise accounting information resource sharing model integrating big data technology has more advantages.

State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the instantaneous network state, the RRM policy takes as input the set of dual variables corresponding to the constraints, which evolve depending on how much the constraints are violated during execution. We theoretically show that the proposed state-augmented algorithm leads to feasible and near-optimal RRM decisions. Moreover, focusing on the problem of wireless power control using graph neural network (GNN) parameterizations, we demonstrate the superiority of the proposed RRM algorithm over baseline methods across a suite of numerical experiments.

Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users’ resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.

Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL agent that receives delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval. Our proposed framework enables agents to make decisions simultaneously and in a distributed manner, unaware of the concurrent decisions of other agents. Moreover, our design of the agents' observation and action spaces is scalable, in the sense that an agent trained on a scenario with a specific number of transmitters and users can be applied to scenarios with different numbers of transmitters and/or users. Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and $5^{th}$ percentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized information-theoretic baseline. We also show that our trained agents are robust and maintain their performance gains when experiencing mismatches between train and test deployments.

LORM: Learning to Optimize for Resource Management in Wireless Networks With Few Training Samples

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, typically results in challenging mixed-integer nonlinear programming (MINLP) problems. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). In contrast to the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning . To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL , which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while providing significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

Engineering Radio Maps for Wireless Resource Management

The term 'radio map' in general refers to the geographical signal power spectrum density, formed by the superposition of concurrent wireless transmissions, as a function of location, frequency and time. It contains rich and useful information regarding the spectral activities and propagation channels in wireless networks. Such information can be leveraged to not only improve the performance of existing wireless services (e.g., via proactive resource provisioning) but also enable new applications, such as wireless spectrum surveillance. However, practical implementation of radio maps in wireless communication is often difficult due to the heterogeneity of modern wireless networks and the sheer amount of wireless data to be collected, stored, and processed. In this article, we provide an overview of the state-of-the-art techniques for constructing radio maps as well as applying them to achieve efficient and secure resource management in wireless networks. Some key research challenges are highlighted to motivate future investigation.

Heterogeneities of Wireless Networks: Radio Resource Management Challenges and Possible Solutions

This paper examines the heterogeneities of wireless networks from six different perspectives, namely mobile terminals, radio access technologies (RATs), network services, users, network operators, and network geographical areas. These different aspects of wireless network heterogeneities offer certain benefits but also pose some challenges to effective management of radio resources. The challenges of wireless network heterogeneities are highlighted in this paper and possible solutions are proffered. The paper then compares recent network selection algorithms proposed for wireless networks with respect to the aspects of heterogeneities considered. Comparative analysis of the different network-selection algorithm shows that whereas RAT, services, and users heterogeneities have received much attention in the literature, terminal, operator, and geographical area heterogeneities have not been widely considered. Thus, more attention should be given terminal, operator, and geographical area aspects of wireless network heterogeneities so that the problems of radio resource management in wireless networks can be addressed in a more holistic way. An algorithm that considers the different aspects of network heterogeneities is proposed, and simulation results are given to highlight the effects of network heterogeneities on connection-level quality of service provisioning in heterogeneous wireless networks.

Matching theory for future wireless networks: fundamentals and applications

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this paper, the first comprehensive tutorial on the use of matching theory, a Nobelprize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then, the key solution concepts and algorithmic implementations of this framework are exposed. Then, the developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.

Strategic bargaining in wireless networks: basics, opportunities and challenges

Strategic bargaining cooperative games have found extensive applications to resource management in wireless networks. In this survey, basics of a strategic bargaining game and solution concepts are firstly presented. Geometrical interpretations are introduced to better understand real meanings of them. Then, the authors survey the applications of various strategic bargaining games for the emerging wireless networks, where the authors concentrate on several interesting problems based on their previous systematic studies: (i) distributed resource management design for cognitive radio networks based on geometrical interpretation of the cooperative solution; (ii) asymmetric bargaining modelling for green communications; (iii) a unified utility tradeoff design between spectral and energy efficiency in heterogeneous cellular networks; (iv) the cooperative rate splitting game for Long Term Evolution-coordinated multi-point system; and (v) a general bargaining formulation with different tradeoffs between efficiency and fairness. In addition, the authors survey the applications of strategic bargaining games to cooperation incentive mechanism, bargaining game on capacity region of interference channel and multiuser and multimedia applications. Finally, challenges and potential research direction are summarised in this work.

Green transmission technologies for balancing the energy efficiency and spectrum efficiency trade-off

As 4G wireless networks are vastly and rapidly deployed worldwide, 5G with its advanced vision of all connected world and zero distance communications is already at the corner. Along with the super quality of user experience brought by these new networks, the shockingly increasing energy consumption of wireless networks has become a worrying economic issue for operators and a big challenge for sustainable development. Green Transmission Technologies (GTT) is a project focusing on the energy-efficient design of physical-layer transmission technologies and MAC-layer radio resource management in wireless networks. In particular, fundamental tradeoffs between spectrum efficiency and energy efficiency have been identified and explored for energy-efficiency-oriented design and optimization. In this article, four selected GTT solutions are introduced, focusing on how they utilize the degrees of freedom in different resource domains, as well as how they balance the tradeoff between energy and spectrum efficiency. On top of the elaboration of separated solutions, the GTT toolbox is introduced as a systematic tool and unified simulation platform to integrate the proposed GTT solutions together.

Optimizing compatible sets in wireless networks through integer programming

In wireless networks, the notion of compatible set refers to a set of radio links that can be simultaneously active with a tolerable interference. Finding a compatible set with maximum weighted revenue from the parallel transmissions is an important optimization subproblem for resource management in wireless networks. For this subproblem, two basic ways of expressing the signal-to-interference-plus-noise-ratio within integer programming are used, differing in the number of variables and the quality of the upper bound given by their linear relaxations. To our knowledge, there is no systematic study comparing the effectiveness of these two approaches to the compatible set optimization problem. The contribution of the article is twofold. First, we present a comparison of the two basic approaches, and, second, we introduce matching inequalities that improve the upper bounds achievable with the two basic approaches. The matching inequalities are generated within the branch-and-cut process using a minimum odd-cut generation procedure based on the Gomory–Hu tree algorithm. The article presents results of a numerical study illustrating our statements and findings.

Adaptive control of call acceptance in WCDMA network

Adaptive control of call acceptance in WCDMA network

Extended proportional fair scheduling for statistical QoS guarantee in wireless networks

Opportunistic scheduling provides the capability of resource management in wireless networks by taking advantage of multiuser diversity and by allowing delay variation in delivering data packets. It generally aims to maximize system throughput or guarantee fairness and quality of service (QoS) requirements. In this paper, we develop an extended proportional fair (PF) scheduling policy that can statistically guarantee three kinds of QoS. The scheduling policy is derived by solving the optimization problems in an ideal system according to QoS constraints. We prove that the practical version of the scheduling policy is optimal in opportunistic scheduling systems. As each scheduling policy has some parameters, we also consider practical parameter adaptation algorithms that require low implementation complexity and show their convergences mathematically. Through simulations, we confirm that our proposed schedulers show good fairness performance in addition to guaranteeing each user's QoS requirements.

Fairness in Radio Resource Management for Wireless Networks

Fairness in Radio Resource Management for Wireless Networks

A Centralized Feedback Control Model for Resource Management in Wireless Networks

In a wireless environment, guaranteeing QoS is challenging because applications at multiple devices share the same limited radio bandwidth. In this paper we introduce and study a resource management model for centralized wireless networks, using feedback control theory. Before applying in practice, the proposed model is evaluated using the well-known 20-sim dynamic system simulator. The experimental results show that flexible and efficient resource allocation can be achieved for a variety of system parameters and WLAN scenarios; however, care should be taken in setting the control parameters and coefficients.

Signalling analysis of cost‐efficient mobility support by integrating mobile IP and SIP in all IP wireless networks

AbstractNext‐generation wireless systems are expected to support efficient IP‐based terminal and personal mobility. Novel mobility‐support architecture called integrated MIP–SIP is designed based on the integration of mobile IP (MIP) and session initiation protocol (SIP), which dynamically combines and leverages the advantages of both protocols in a cost‐effective way. The design motivation and methodology is to make full use of standardized work from both protocols, select composite processes that are more efficient for common functions, integrate similar entities and procedures to reduce redundancies, and avoid further duplicate standardization. By these means, the system efficiency can be greatly improved. The essential mobility procedures presented are applicable to both MIPv4 and MIPv6, though IPv6 is focused on. Both analytical and simulation results show that the proposed architecture outperforms existing hybrid MIP–SIP and pure SIP mobility approaches in terms of a clear‐cut reduction in mobility signalling loads, which is crucial to the resource management in wireless networks. Copyright © 2006 John Wiley & Sons, Ltd.

An efficient and fair cooperative approach for resource management in wireless networks

In cellular networks, the implementation of various resource management processes, such as bandwidth reservation and location updates, has been based on the one-to-one resource management information exchange paradigm, between the mobile nodes and the base stations. In this paper, we design and demonstrate the use of a distributed cooperative scheme that can be applied in the future wireless networks to improve the energy consumption for the routine management processes of mobile terminals, by adopting the peer-to-peer communication concept of wireless ad hoc networks. In our approach, the network is subdivided into one-hop ad hoc clusters where the members of each cluster cooperate to perform the required management functions, and conventional individual direct report transmissions of the mobile terminals to the base stations are replaced by two-hop transmissions. The performance evaluation and the corresponding numerical results presented in this paper confirm that our proposed scheme reduces significantly the overall system energy consumption when compared with the conventional one-to-one direct information management exchange approach. Furthermore the issue of fairness in dynamically selecting the various cluster heads in successive operational cycles of the proposed scheme is analyzed, and an enhanced algorithm is proposed and evaluated, which improves significantly the cluster head selection fairness, in order to balance the energy consumption among the various mobile terminals. Copyright © 2005 John Wiley & Sons, Ltd.

Max-utility wireless resource management for best-effort traffic

Due to the characteristics of wireless channels, utility-based resource management in wireless networks requires a set of mechanisms that are different from those for wireline networks. This paper explores in detail why and how the requirements are different. In particular, we analyze the wireless network performance to find out the scheduling algorithm that maximizes total utility of the system. Unlike previous studies, this paper focuses on scenarios in which wireless networks are not fully loaded and all of the users are best-effort data users, i.e., there is no streaming user. Our first key conclusion is that Kleinrock's Conservation Law provides a valuable means to accurately capture the perceived rates of best-effort users in such systems. The queueing analysis further indicates that, within periods during which channel conditions are stable for each user, albeit differ from user to user, the max-utility scheduling algorithm can be derived using queueing theorem and can be readily implemented in actual systems for utility functions that are of exponential or log format. When further taking into account the time-variant nature of wireless channel conditions, our simulation results demonstrate that dynamic weighted fair queueing, with weights adjusted according to the channel conditions, can achieve highly desirable performance with great flexibility.

Fundamental limitations of power control and radio resource management in wireless networks

AbstractThe ultimate resource in wireless networks is transmission power. The system stability and the resource sharing algorithms all rely on well functioning power control. To realistically address resource management algorithms, it is important to consider fundamental limitations of the radio resource and of power control. Exact and approximate capacity expressions are derived and related to Quality of Service (QoS) requirements of services. Power control is subject to limited update rate, limited feedback bandwidth, time delays, measurement errors, feedback errors and filtering effects which all affect the resulting performance. Simulations further illustrate the hampering effects and motivates models of power control inaccuracies. Copyright © 2004 John Wiley & Sons, Ltd.