Energy-Efficient Resource Allocation in Wireless Networks

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.

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.

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

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.

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 a wireless communications network, the movement of mobile users presents significant technical challenges to providing efficient access to the wired broadband network. In this paper, we construct a new analytical/numerical model that characterizes mobile user behavior and the resultant traffic patterns. The model is based on a semi-Markov process representation of mobile user behavior in a general state-space. Using a new algorithm for parameter estimation of a general Hidden Semi-Markov Model (HSMM), we develop an efficient procedure for dynamically tracking the parameters of the model from incomplete data. We then apply our integrated model to obtain estimates of the computational and bandwidth resources required at the wireless/wired network interface to provide high performance wireless Internet access and quality-of-service to mobile users. Finally, we develop a threshold-based admission control scheme in the wireless network based on the velocity information that can be extracted from our model.

Resource allocation in wireless networks, i.e., assigning time and frequency slots over specific terminals under spatio-temporal constraints, is a fundamental and challenging problem. Belief Propagation/message passing (inference) algorithms have been proposed for constraint satisfaction problems (CSP), since they are inherently amenable to distributed implementation. This work compares two message passing algorithms for time and frequency allocation, satisfying signal-to-interference-and-noise-ratio, half-duplex-radio operation and routing constraints. The first method periodically checks whether the constraints are satisfied locally and restarts specific messages, when the local constraints (encoded in corresponding factors) are not satisfied. The second method stochastically perturbs Belief Propagation, using Gibbs sampling. The methods are evaluated, based on how often they fail to converge to a valid (i.e., constraint-satisfying) allocation, coined as outage probability. Numerical results demonstrate that, as the maximum number of iterations increase, both methods decrease the outage probability. However, the restarting method offers faster convergence to a valid CSP solution. Future work will focus on next generation 5/6G wireless networks.

This paper presents a comprehensive approach to solve the problem of joint dynamic resource allocation (JDRA) in heterogeneous wireless networks using a Hopfield neural network (HNN). A generic formulation for packet services with delay constraints is proposed to decide the optimal bit rate and radio access technology (RAT) allocation. Some illustrative simulations results in a basic scenario are presented to evaluate performance of the proposed algorithm.

Power allocation across users in two adjacent cells is studied for a wireless code division multiple access (CDMA) network with mixed voice and data services. We assume that each user has a utility function that measures the user's satisfaction, or utility, as a function of the received signal-to-interference-plus-noise ratio (SINR). Each particular service (voice or data) is associated with a different utility function. We consider the forward link. Our objective is to allocate transmitted power to maximize the total utility summed over all active users subject to rate and power constraints. We show that the maximum utility can be achieved with a pricing scheme. We characterize the solution to a one-cell utility maximization problem with fixed interference from the other cell. For two-cell utility maximization, the two cells must cooperate to achieve the maximum utility.

— 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.

Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results. In this work we propose a stateless variation of Q-learning, which we apply to exploit spatial reuse in a wireless network. In particular, we allow networks to modify both their transmission power and the channel used solely based on the experienced throughput. We concentrate in a completely decentralized scenario in which no information about neighbouring nodes is available to the learners. Our results show that although the algorithm is able to find the best-performing actions to enhance aggregate throughput, there is high variability in the throughput experienced by the individual networks. We identify the cause of this variability as the adversarial setting of our setup, in which the most played actions provide intermittent good/poor performance depending on the neighbouring decisions. We also evaluate the effect of the intrinsic learning parameters of the algorithm on this variability.

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.

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.

A novel optimization framework for resource allocation in wireless networks and radar systems is proposed, which merges the methods of maximum block improvement (MBI) and of sequential optimization. A detailed convergence proof is provided, showing that the proposed algorithm is able to monotonically increase the objective value while ensuring that every limit point of the generated variable sequence fulfills the problem first-order optimality conditions under very mild hypothesis. These results extend available convergence results on MBI and sequential optimization, significantly widening the range of applications that can be handled by the proposed framework compared to available approaches. This point is illustrated in detail presenting relevant applications from both the cellular and radar context, which fall under the umbrella of the developed optimization method.

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.