Deep neural network based resource allocation in D2D wireless 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.

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.

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.

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

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.

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.

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.

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.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

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.

Future wireless networks are expected to support diverse Internet of Things (IoT) applications under dynamic network conditions through effective resource allocation. However, the growing complexity of underlying optimization problems for resource allocation has brought many challenges to traditional centralized network operations due to inherent computational constraints. To overcome these challenges, this paper proposes a Distributed-Optimization with Centralized-Refining (DO-CR) mechanism to achieve more efficient resource allocation by engaging both access point and all devices. Specifically, the new DO-CR mechanism first utilizes the distributed processing capacity of all devices, allowing them to optimize their own resource allocation schemes through a new resource reservation and reporting technique. Then a centralized optimizer generates a graph of resource trading topology based on individual optimization results and achieves the Pareto optimal solution by the graph-based algorithm. This Pareto optimal solution simplifies the overall optimization problem and enables the central optimizer to solve it with smaller feasible regions. The analysis presents that the DO-CR mechanism’s performance is bounded by Pareto optimality as lower limit and global optimality as upper limit. Simulation results demonstrate that the proposed DO-CR mechanism significantly reduces processing time on the centralized optimizer while maintaining near-optimal utility performance compared to conventional optimization methods.

With the popularity of versatile mobile multimedia applications and the pressing need to reduce energy consumption in future wireless networks, new challenges have been posed to maintain adequate coverage, quality of service, and reliability for big-scale multimedia traffic. Specifically, in this paper we introduce a cooperative multimedia relay framework and investigate premium-regular-diversity-based multimedia resource allocation treatment approaches. We also comparatively study five generic wireless multimedia relay scenarios including direct transmission, single relay, multiple pure relays, relay cooperation with space-time coding, and relay coordination with distributed beamforming. The key contribution of the new relay resource allocation framework is that packet importance priority diversity at the application layer is jointly considered with spatial diversity and relay coordination protocols at lower layers. Premium packets and regular packets of multimedia streams are adaptively allocated to relay nodes, and transmission resources are optimally allocated with regards to energy budget. Extensive simulation results demonstrate that the proposed resource allocation paradigm using multirelay coordination has significant video quality enhancement and energy saving potentials to support future big-data wireless multimedia traffic.

QoS provisioning for Wireless Mesh Networks (WMN) is a very desirable objective, since it requires great thoughtfulness, scalability and thoroughfull analysis. In this paper, numerical and statistical analysis of packet-level resource allocation with effective QoS provisioning for WMS approach is being introduced. Power allocation, packet scheduling, subcarrier allocation, and QoS support are investigated and analyzed in combined Karush-Kuhn-Tucker (KKT)-driven and a genetic algorithm (GA)-based approach, that achieves a desired balance between time complexity and system performance.

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.

We discuss the idea of waveform flexibility and resource allocation in future wireless networks as a promising tool for network slicing implementation down to the lowest layers of the OSI (Open Systems Interconnection) models. In particular, we consider the possibility of cognitively adjusting the shape of the waveform to the requirements associated with various network slices. Moreover, such an adjustment of waveform shape is realised jointly with the selection and allocation of the appropriate frequency bands to each slice. In our approach, the definition of the waveform, as well as the assignment of resources, is done based on the information about the surrounding environment and each slice requirement stored in a dedicated context-information database. In this paper, we present the key concept of waveform flexibility for network slicing, the proposed algorithm for waveform selection and resource allocation among slices, and the achieved simulation results.