Resource Allocation in Green Dense Cellular Networks: Complexity and Algorithms

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.

User-centric overlapped clustering, relying on base station (BS) cooperation, is a promising architecture for densely deployed BSs in millimeter-wave (mmWave) networks. In this architecture, a user can be served by a set of cooperating BSs which reduces the interference received from neighboring BSs. This paper studies the problem of maximizing the number of served users in a dense mmWave network while guaranteeing the quality of service (QoS) required by each UE, defined by a received signal quality. Since the formulated problem is NP-hard, two near-optimal solutions are proposed that perform clustering and resource allocation. The first is a heuristic algorithm that builds the clusters by greedily associating the user with as many BSs as needed. The second approach is a binary particle swarm optimization (PSO) algorithm adapted to our constrained problem. Simulations confirm that the proposed algorithms approach the optimal solution with substantially lower computational complexity.

This paper is devoted to studying the processes of resource allocation in dynamic resource networks. In such networks, the capacities of the arcs depend on time. Resource allocation in the network occurs in discrete time. The resource of each vertex is distributed only between adjacent vertices according to some rules. The study of the processes of resource redistribution in such networks is carried out. The main goal is to develop methods for finding the limit state (distribution) of a resource in a dynamic resource network. It is shown that the approach based on the construction of an auxiliary network is also applicable to reduce the problem of resource allocation in a dynamic network to a similar problem in an auxiliary network. Theorems on the existence of a limit state on an auxiliary graph are proved for strongly regular periodic dynamical networks. To find the limit states, one can use the approaches which are developed for the shortest path problem in dynamic networks.

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU’s throughput by up to 9 times and 22% in comparison to these both schemes.

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.

We present a general framework for the problem of quality-of-service (QoS) routing with resource allocation for data networks. The framework represents the QoS parameters as functions rather than static metrics. The formulation incorporates the hardware/software implementation and its relation to the allocated resources into a single framework. The proposed formulation allows intelligent adaptation of QoS parameters and allocated resources during a path search, rather than decoupling the path search process from resource allocation. We present a dynamic programming algorithm that, under certain conditions, finds an optimal path between a source and destination node and computes the amount of resources needed at each node so that the end-to-end QoS requirements are satisfied. We present jitter and data droppage analyzes of various rate-based service disciplines and use the dynamic programming algorithm to solve the problem of QoS routing with resource allocation for networks that employ these service disciplines.

With the access of massive terminals of the Internet of Things (IoT), the low-power wide-area networks (LPWAN) applications represented by Long Range Radio (LoRa) will grow extensively in the future. The specific Long Range Wide Area Network (LoRaWAN) protocol within the LoRa network considers both low power consumption and long-range communication. It can optimize data transmission to achieve low communication latency, ensuring a responsive system and a favorable user experience. However, due to the limited resources in LoRa networks, if certain terminals have heavy traffic loads, it may result in unfair impacts on other terminals, leading to increased data transmission latency and disrupted operations for other terminals. Therefore, effectively optimizing resource allocation in LoRa networks has become a key issue in enhancing LoRa transmission performance. In this paper, a Mixed Integer Linear Programming (MILP) model is proposed to minimize network energy consumption under the maximization of user fairness as the optimization goal, which considers the constraints in the system to achieve adaptive resource allocation for spreading factor and transmission power. In addition, an efficient algorithm is proposed to solve this optimization problem by combining the Gurobi mathematical solver and heuristic genetic algorithm. The numerical results show that the proposed algorithm can significantly reduce the number of packet collisions, effectively minimize network energy consumption, as well as offering favorable fairness among terminals.

The authors consider the problem of dynamic channel allocation in cellular networks. Each cell can use any channel, subject to the interference constraints. Channel allocation algorithms are executed by the network switch in a centralized way. The authors show how to design and use objective functions aimed at proper channel allocation and improvement of network performance. As a figure of merit of network performance, they consider the blocking probability in the network as a whole, and the maximum blocking probability in any particular cell of the network ("hot-spot" in the network). They designed three specific channel allocation policies, based on three different objective functions. Compared with two other benchmark policies, the approach shows significant improvement. Performance analysis of various channel allocation policies is virtually impossible without simulations, which are prohibitively time-consuming in the case of small blocking probabilities. The authors propose an original approximate method based on a short simulation and an analytic approximation. The method exhibits good accuracy and significant improvement in efficiency.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The cross-layer resource allocation problem in wireless multi-hop networks (WMHNs) has been extensively studied in the past few years. Most of these studies assume that every node has the perfect channel state information (CSI) of other nodes. In practical settings, however, the networks are generally dynamic and CSI usually becomes outdated when it is used, due to the time-variant channel and feedback delay. To deal with this issue, we study the cross-layer resource allocation problem in dynamic WMHNs with outdated CSI under channel conditions where there is correlation between the outdated CSI and current CSI. Two major contributions are made in this work: (1) a closed-form expression of conditional average capacity is derived under the signal-to-interference-plus-noise ratio (SINR) model; (2) a joint optimization problem of congestion control, power control, and channel allocation in the context of outdated CSI is formulated and solved in both centralized and distributed manners. Simulation results show that the network utility can be improved significantly using our proposed 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.

A mobile network is a distributed communication system that uses radio signals as its information medium. Since the popularization of mobile networks, the rate of connectivity has soared and the subsequent load on the networks is much more significant than original projections. Hence, resource management becomes an important issue when designing mobile networks. This work concentrates on the most important resource available to mobile networks, that is, the limited allocated bandwidth. With the given amount of bandwidth, the network is expected to meet a minimum quality of service, to provide subscribers with access to the network. As the network is based on a cellular design, the way to improve utilization of the bandwidth is to reuse frequencies or channels. However, this reuse comes at a cost. The allocation of reused channels must be subject to the avoidance of interference between allocated channels. This is known as the channel allocation problem (CAP). It will be shown that the problem can be formalized into an optimization problem, for which there are many solution methodologies. In order to meet the demands of solution quality and algorithm efficiency, the decision of which method to use is critical. Biologically inspired techniques provide effective search strategies that enable the discovery of near optimal solutions. They are ideal for problems with large and complex solution spaces such as the CAP. However, the performance of these algorithms in terms of solution quality and efficiency varies greatly with the problems to which they are applied. Copyright © 2003 John Wiley &amp; Sons, Ltd.

LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks.

The automobile industries are undergoing a technological revolution, which has facilitated the emergence of multiple Vehicle-to-Everything (V2X) applications with diverse Quality of Service (QoS) requirements. The conventional resource allocation methods are no longer applicable in V2X due to the diverse vehicular services with different QoS requirements in various practical scenarios. Therefore, this paper proposes a multi-service resource allocation strategy for V2X. Specifically, the problem of allocating resources for multiple V2X services is decoupled into two sub-problems, i.e., resource allocation between slices, and resource scheduling within slice. Then, a heuristic resource allocation algorithm is adopted to solve the resource allocation between slices sub-problem. For Real-Time (RT) and Non-Real Time (NRT) services in the vehicular network, specialized scheduling has been designed for the near-real-time Radio Access Network (RAN) Intelligent Controller and non-real-time RAN Intelligent Controller in the Open Radio Access Network (O-RAN) architecture, to meet the heterogeneous QoS service requirements of vehicles. Simulation results demonstrate that compared to the modified largest weighted delay first (M-LWDF) algorithm, the proposed resource allocation strategy can enhance the AoI of the proportional fairness (PF) services by 15%, and provide 7.39% transmission rate gain for NRT services.

The advent of sixth-generation (6G) mobile networks introduces unprecedented demands for ultra-low latency, terabit-per-second throughput, and dynamic resource allocation across heterogeneous services such as URLLC, eMBB, and mMTC. Traditional resource management approaches lack the adaptability and predictive intelligence required to operate under such conditions. This study proposes a comparative analysis of deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer architectures—for intelligent resource allocation in 6G environments. Synthetic traffic patterns and benchmark datasets were used to emulate realistic 6G workloads, and models were evaluated using key performance metrics: accuracy, latency, throughput, energy efficiency, and fairness index. Simulations were conducted in Python using TensorFlow and PyTorch frameworks within SDN-based virtualized environments to replicate programmable 6G infrastructures. The proposed hybrid system, integrating CNN, RNN, and Transformer modules, achieved latency reduction of 38%, throughput improvement of 22%, energy savings of 26%, and fairness index gain of 18% compared to baseline models. These findings demonstrate the viability of deep learning for scalable, adaptive, and sustainable resource allocation in next-generation mobile networks. The study contributes a reproducible simulation framework, a performance benchmark across models, and a foundation for future research in federated learning, edge AI, and 6G deployments.