Distributed-Optimization With Centralized-Refining for Efficient Resource Allocation in Future 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.

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.

The problem of optimal resource allocation in wireless networks usually involves scheduling of the network independent sets (ISs), of which the number increases exponentially in the network scale. To deal with such large-scale optimization problems, traditional approaches often resort to some heuristics or iterative algorithms for obtaining a relatively small set of ISs to solve the problems, but at the cost of suboptimality or long convergence time. In this paper, we consider wireless network resource allocation in dynamic flow environments, aiming at maximizing the network capacity. We propose a learning-based approach to find ISs based on the dynamic flow demands. Specifically, instead of searching for individual ISs, we propose to learn groups of ISs by using a deep belief network (DBN). We present detailed design of the DBN-based learning method including details in the offline training and online running phases. Simulation results demonstrate that our DBN-based method outperforms existing ones in terms of achieved network capacity and computation time.

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.

In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.

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 in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.

Effective resource allocation has always been one of the serious challenges in wireless communication. A considerable number of machine type communication (MTC) devices in 5G with variable quality of service (QoS) aggravates this challenge even further. Existing Resource allocation schemes in MTC are usually considering signal to noise ratio (SNR), which provides preference to MTC devices based on distance rather than their QoS requirements. This paper proposes a resource allocation scheme with dynamic priorities for MTC devices with multiple radio access technologies (RATs). The proposed resource allocation scheme has two main parts namely medium access and resource allocation. The medium access leverages the broadcast nature of wireless signal and MTC devices' wait time to assign priorities using capillary band in a secure and integral way. At resource allocation, SNR, total induced transmission delay, and transmission-awaiting MTC devices are used to assign resources in the cellular band. The rumination of two-staged dynamic priorities in the proposed scheduling scheme brings significant performance improvements in outage and success probabilities. Compared to SNR-based schemes, the proposed mechanism performs well by expressively improving the outage and success probability by 20% and 30%, respectively.

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.

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.

In this paper, a cross-layer resource allocation mechanism is proposed for wireless multimedia service.In particular, a game theory based on quality of service (QoS) for multimedia users is introduced to deal with the fairness of network resource allocation in wireless networks.Moreover, the channel states of wireless users are additionally regarded under the cross-layer design in WiMAX environment.In details, the bargaining solution is adopted to discover the efficient and fair resource allocation strategy for multimedia service in considering QoS in the peak signal-to-noise ratio (PSNR) and the channel states in the carrier-to-interface ratio (CINR).The proposed mechanism is illustrated and evaluated by simulation results of transmitting video sequences in WiMAX environment.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

Underlay device-to-device (D2D) communication in cellular networks has been considered as a promising technique that can improve the spectral efficiency of cellular systems and meet the growing demand for wireless local services. In underlay D2D, it is of primary importance to manage the mutual interference between cellular links and D2D links through effective resource allocation. While most of previous works on D2D resource allocation are developed based on the knowledge of the channel state information (CSI) on the interference channels as well as the desired channels, it is hard to obtain full CSI in practice. Accordingly, we consider D2D resource allocation schemes based on distance between nodes. In particular, we formulate two optimization problems for D2D resource allocation using the outage probability computed based on the distance information as cost functions. One is a linear sum assignment problem (LSAP) and the other is a linear bottleneck assignment problem (LBAP). By applying the graph theory, we provide efficient algorithms for solving the optimization problems. Numerical results are provided to show the effectiveness of the proposed optimization as compared to previously proposed distance-based resource allocation algorithms.

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.

— This article presents a time-aware admission control and resource allocation scheme in wireless networks, in the context of a future generation cellular network. The quality levels (and their respective utility) of different connections are specified using discrete resource-utility (R-U) functions. The scheme uses these R-U functions for allocating and reallocating bandwidth to connections, aiming to maximise the accumulated utility of the system. However, different applications react differently to resource reallocations. Therefore at each allocation time point the following factors are taken into account: the age of the connection, a disconnection (drop) penalty and the sensitiveness to reallocation frequency. The evaluation of our approach shows a superior performance compared to a recent adaptive bandwidth allocation scheme (RBBS). In addition we have studied the overhead that performing a reallocation imposes on the infrastructure. To minimise this overhead, we present an algorithm that efficiently reduces the number of reallocations, while remaining within a given utility bound.