QoS analysis of resource allocation in wireless mesh networks

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

Problem statement. Due to their mobility, flexibility, ease of deployment, and low cost, Autonomous Aerial Vehicle (AAV) play an important role in future wireless networks. However, their pratical implementation faces challenges, including energy constraints, dynamic channel variations, interference management, and the need for efficient resource allocation to ensure seamless connectivity for ground users. Traditional optimization methods often fail to adapt to these complexities in real-time, limiting the effectiveness of AAV-assisted wireless networks. The aim of the work provides a comprehensive review of resource allocation in AAV-assisted wireless networks, focusing on power and bandwidth optimization strategies, as well as the key challenges in ensuring efficient and reliable communication. Methods: used in this study include a systematic review of existing literature, analyzing optimization approaches such as Game Theory, Artificial Intelligence for efficient resource allocation in AAV-assisted wireless networks. Novelty: this study analyzes resource allocation challenges in AAV-assisted network, focusing on the interdependence of power and bandwidth allocation. It explores optimization techniques like Game Theory, and Artificial Intelligence. Results. The analysis in this paper demonstrates that Game Theory and Artificial Intelligence based approaches significantly improve resource allocation efficiency. Additionally, the study identifies key challenges, including heterogeneous density network, security concerns, and complex channel modeling, providing insights for future research. Practical / Theoretical Relevance: This study advences the theoretical understanding of resource allocation in AAV-assisted wireless networks by integrating optimization strategies from Game Theory and Artificial Intelligence. Pratically, it provides insight into enhancing network efficiency, adaptability and security, making AAV-based communication more viable for real-world applications, such as disaster recovery, remote areas coverage, and IoT data collection.

Next generation high data rate applications such as High-Definition (HD) video transmission place a heavy load on wireless network resources. To achieve the strict quality of service requirements of HD video in a multi-user network requires efficient resource allocation. Rate-distortion theory provides an appropriate basis for this. The Nash Bargaining Solution (NBS) is proven to generate an optimal solution to a rate-distortion based convex resource allocation problem. Particle Swarm Optimization (PSO) is a meta-heuristic optimizer capable of generating near-optimal solutions. PSO, however, is not constrained to convex optimization problems. The motivation of this work is to demonstrate a PSO implementation capable of achieving an optimal solution to the resource allocation problem. The capability of the PSO approach to reach an optimal solution suggests its potential for resource allocation in a network of heterogeneous applications where convex optimization methods do not apply.

SummaryIn this paper, we present a QoS‐driven energy efficient power and subcarrier allocation in green orthogonal frequency division multiple access networks. Our proposed scheme aims at maximizing the effective energy efficiency (EEE), defined as overall effective capacity per total power consumption, subject to given user delay‐QoS requirements and an average sum transmit power constraints. We analytically obtain the uniquely global optimal power and subcarrier using fractional programming, which transforms the fractional program into an equivalent parametric convex program that has a tractable solution. Analytical results show that the optimal solution has the same structure as effective capacity maximization and overall power minimization solutions. Simulation results show that there exists a trade‐off between EEE and delay. When the delay is more stringent, the EEE decreases. Moreover, when the number of subcarriers increase, EEE increases, where a fixed optimal EEE can be achieved regardless of stringency of delay‐QoS requirements. Furthermore, our proposed resource allocation provides significant EEE gains over both the independent resource allocation and overall power minimization schemes. Copyright © 2015 John Wiley & Sons, Ltd.

An overview of game-theoretic approaches to energy-efficient resource allocation in wireless networks is presented. Focusing on multiple-access networks, it is demonstrated that game theory can be used as an effective tool to study resource allocation in wireless networks with quality-of-service (QoS) constraints. A family of non-cooperative (distributed) games is presented in which each user seeks to choose a strategy that maximizes its own utility while satisfying its QoS requirements. The utility function considered here measures the number of reliable bits that are transmitted per joule of energy consumed and, hence, is particulary suitable for energy-constrained networks. The actions available to each user in trying to maximize its own utility are at least the choice of the transmit power and, depending on the situation, the user may also be able to choose its transmission rate, modulation, packet size, multiuser receiver, multi-antenna processing algorithm, or carrier allocation strategy. The best-response strategy and Nash equilibrium for each game is presented. Using this game-theoretic framework, the effects of power control, rate control, modulation, temporal and spatial signal processing, carrier allocation strategy and delay QoS constraints on energy efficiency and network capacity are quantified.

Wireless mesh networking is an emerging technology for future broadband wireless access. Future wireless networking can benefit from a robust and reliable wireless mesh backbone rendered by mesh routers, providing an all-wireless ambience. Due to the requisite multichannel communications for high-speed data transmissions, power allocation for opportunistically exploiting fading wireless channels, and packet scheduling for QoS provisioning, joint power-frequency-time resource allocation is indispensable. In this article we propose a low-complexity intracluster resource allocation algorithm, taking power allocation, subcarrier allocation, and packet scheduling into consideration. Numerical results demonstrate that our scheme is near optimal, and that our optimality-driven resource allocation approach outperforms a greedy algorithm, working out a better performance compromise among throughput, packet dropping rate, and packet delay.

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study.

Multi-channel communication in a Wireless Mesh Network with routers having multiple radio interfaces significantly enhances the network capacity. Efficient channel assignment is critical for realization of optimal throughput in such networks. This paper addresses the channel assignment problem for multi-channel multi-interface (radio) wireless mesh networks. We focus on static wireless mesh networks where multiple non-overlapping channels are available for each wireless interface. In this network environment, our objective is to find a fixed channel assignment which maximizes the number of bidirectional links that can be activated simultaneously, subject to interference constraints. We present a mixed integer linear programming models for solving the fixed channel assignment problem with multiple radios. Detailed computational results are presented and discussed to validate the algorithm.

Developing effective, fairness-preserving optimization algorithms is of considerable importance in systems which serve many users. In this paper we show the results of the threshold accepting procedure applied to extremely difficult problem of fair resource allocation in wireless mesh networks (WMN). The fairness is modeled by allowing preferences with regard to distribution of Internet traffic between network participants. As aggregation operator we utilize weighted ordered weighted averaging (WOWA). In the underlaying optimization problem, the physical medium properties cause strong interference among simultaneously operating node devices, leading to nonlinearities in the mixed-integer pricing subproblem. That is where the threshold accepting procedure is applied. We show that, the threshold accepting heuristic performs much better than the widely utilized simulated annealing algorithm.

The paper describes a Resource allocation in cellular networks using orthogonal frequency division multiple access (OFDMA) systems. Both center cell and edge cell users using same power, edge cell users suffer from Inter Cell Interference (ICI). Power allocation cell-edge user's power is maximized automatically reduce the inter cell interference under the condition that desirable performance for cell-center users must be maintained. The power optimization is formulated as a Greedy Power allocation method. Simulation results indicate that our proposed scheme can achieve significantly balanced performance improvement between cell-edge and cell-center users in multi-cell networks, and goal of future wireless networks in terms of providing high performance to anyone from anywhere.

In wireless networks many nodes contend for available resources creating a challenge in resource allocation. With shared resources, fairness in allocation is a serious issue. Fairness metrics have been defined to measure the fairness level of resource decisions in allocations. Therefore, fairness metrics significantly influence network and node performances emphasizing the need for due diligence to fairness metrics. It can be seen that fairness metrics in many of the allocation strategies and algorithms in the literature are not analyzed in depth to show the overall fairness of allocations from different perspectives. Hence, we propose a series of fairness metrics for resource allocation in wireless networks, which evaluate individual, system, short and long term fairness. Our metrics are general enough to be adapted in either multiple or single resource sharing scenarios. Algorithms using these metrics for channel allocation problem in peer-to-peer wireless regional area network is proposed and simulated, and the results show that our metrics in conjunction with smart channel allocation strategies guarantee both the fairness and performances.

The increased complexity of future 5G wireless communication networks presents a fundamental issue for optimal resource allocation. This continuous, constrained optimal control problem must be solved in real-time since the power allocation should be consistent with the instantly evolving channel state. This paper emphasizes the application of deep learning to develop solutions for radio resource allocation problems in multiple-input multiple-output systems. We introduce a supervised deep neural network model combined with particle swarm optimization to address the issue using heuristic-generated data. We train the model and evaluate its ability to anticipate resource allocation solutions accurately. The simulation result indicates that the trained DNN-based model can deliver the nearoptimal solution.

Cross-layer design approaches are critical for efficient utilization of the scarce radio resources with QoS provisioning in the third-generation wireless networks and beyond. Better system performance can be obtained from information exchanges across protocol layers, which may not be available in the traditional layering architecture. This article provides an overview of cross-layer design approaches for resource allocation in 3G CDMA networks, summarizes state-of-the-art research results, and suggests further research issues. In addition, a cross-layer design approach for real-time video over time-varying CDMA channels is proposed, where link layer resource allocation benefits from information in both the application and physical layers. Simulations results are given to demonstrate the effectiveness of the proposed approach.

Проблематика. Сьогодні мобільний зв'язок зайнятий наданням IP-сервісів та передаванням мультимедійного контенту з одного місця в інше, але вже завтра нове покоління 5G зможе контролювати широкий спектр матеріальних об'єктів у реальному часі із лише незначним втручанням людини. Створюється так званий Інтернет речей «IoT» із безліччю нових привабливих програм. У поданій роботі розглянутий новий підхід до розподілу ресурсів для майбутніх безпроводових локальних мереж у складі мереж 5G.
\nМета досліджень. DIDO (Distributed Input Distributed Output) – нова технологія, яка направлена на надання послуг гнучкої багатокористувацької безпроводової мережі в усьому світі у межах міжнародного узгодження і конвенцій для використовуваних частот серед інших мобільних технологій для інтеграції з 5G. Техніка DIDO націлена на розподіл ресурсів для майбутніх мереж безпроводового зв'язку, які можуть використовувати широкий спектр «нетрадиційних» частот від 3 МГц до 30 ГГц і працювати незалежно одна від одної із повною пропускною спроможністю і високою швидкістю передачі даних на відстанях від 100 м до 1000 км.
\nМетодика реалізації. Був проведений аналіз усіх відомих публикацій, які присвячені новому поколінню мобільного зв'язку 5G. Досліджувались аспекти взаємодії 5G із WLAN, BT, WSN та іншими мікро- и пікорадіомережами. Розглянутий новий підхід до розподілу частотних ресурсів для майбутньої WLAN.
\nРезультати досліджень. Представлений огляд, а також обговорюються основні напрямки розробки майбутніх мереж мобільного радіозв'язку 5G за стандартом IMT 2020. Вони включають, окрім іншого, перспективну технологію DIDO (Distributed Input Distributed Output) для розподілу ресурсів для майбутніх мереж безпроводового зв'язку. Зазначена технологія повинна тільки розширити існуючу інфраструктуру мобільного радіозв'язку GERAN, UTRAN, SAE і IMS попередніх поколінь 2G – 4G за допомогою гнучкої «всесвітньої» WLAN, яка працює з використанням баз даних для доступних діапазонів частот та з доступом до Web-контенту на основі так званих DIDO-центрів (DIDO Data Centers). 
\nВисновки. Эфективні WLAN, у тому числі із використанням техноології DIDO, повинні стати частиною нових систем покоління 5G. У якості нового метода розподілу ресурсів для майбутніх безпроводових локальних мереж, з точки зору деяких провідних компаній, таких як Rearden, Microsoft, Samsung, Huawei, має бути використаний метод DIDO.

Wireless mesh networking has trans pired as a gifted technology for potentialbroadband wireless access. In a communication network, wireless mesh network plays a vital role in transmission and are structured in a mesh topology. The coordination of mesh routers and mesh clients forms the wi reless mesh networks which are routed through the gateways.Wireless mesh networks uses IEEE 802.11 standards and has its wide applications broadband home networking and enterprise networking deployment such as Microsoft wireless mesh and MIT etc. A profes sional Qos provisioning in intra cluster packet level resource allocation for WMN approach takes power allo cation, sub carrier allocation andpacket scheduling. This approach combines the merits of aKarush-Kuhn-Tucker (KKT) algorithm and a genetic algorithm (GA) based approach. The KKT algorithm uses uniform power allocation over all the subcarriers, based on the optimal allocation criterion. The genetic algorithm is used to generate useful solutions to optimization and search problems and it is also used for search problems. By combining the intrinsic worth of both the approaches, it facilitates effective QOS provisioning at the packet level. It is conclude d that, this approach achieves a preferred stability between system implementation and computational convolution.