Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations

Azizi, Arman; Parsaeefard, Saeedeh; Reza, Javan, Mohamad; Mokari, Nader; Yanıkömeroğlu, Halim

doi:10.1109/tmc.2019.2926713

Cited by 17 publications

(20 citation statements)

References 43 publications

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“…However, the available resources in UAVs are limited and they should be properly managed so that they can provide better performance. In an UAV-assisted network, the locations of UAVs and the way they change their locations, affect the power consumption and the Quality of Service (QoS) [2].…”

Section: A Motivationmentioning

confidence: 99%

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

Pourghasemian,

Abedi,

Salarhosseini

et al. 2021

Preprint

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In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on user's mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some Virtual Network Functions (VNFs) to migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, placement, and scheduling the requested service's functions over the UAVs and perform suitable VNF migration. Then we propose a novel Hierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcement learning method to solve our problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the request reject rate and average delay by 31.5% and 20% and increases the energy efficiency by 40% compared to DDPG method.

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Section: A Motivationmentioning

confidence: 99%

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

Pourghasemian,

Abedi,

Salarhosseini

et al. 2021

Preprint

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“…The related literature has been restricted to either uncapacitated network structures or fixed service-levels. In [4], a heterogeneous wireless network with UAV-BSs is designed to improve network profit. In [11], the UAV-BSs are considered to serve a number of given hotspots with random users and the optimal pricing for a single service-level is studied.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the following, we relax this assumption and add a cost factor to profit model. There exist a number of methods in the literature, in which the profit (or utility in our case) function includes the costs related to resource utilization, i.e., the resource allocated to users, such as bandwidth [4] or energy [37]. Since our model requires the available bandwidth to be allocated, we modify the original utility function by adding the cost of bandwidth.…”

Section: ) Effects Of Incorporating the Cost Of Bandwidth To The Profit Modelmentioning

confidence: 99%

“…Similar to resource management strategies in classical wireless communications such as cache-enabled base stations [3], it is a significant problem in UAV-assisted WCNs to efficiently manage resources. Therefore, there has been a number of attempts to jointly optimize the location and resource allocation decisions [4]. Indeed, it is important to take the finite backhaul capacity of the UAV-BSs in consideration.…”

Section: Introductionmentioning

confidence: 99%

“…Although there have been some attempts to consider network profit as an objective in 5G [4], this concept still needs to be thoroughly analyzed as the next generation WCNs are expected to embrace different pricing models. For instance, congestion-based [7] or tiered pricing models [8] are shown to be effective models to mitigate the burden on resource allocation decisions in the networks by charging higher prices for users whose demand are exhausting the network resources.…”

Section: Introductionmentioning

confidence: 99%

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Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization

et al. 2020

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Unmanned Aerial Vehicle Base Stations (UAV-BSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) with a potential to create opportunities for enhancing the capacity of the network by dynamically moving the supply towards the demand while facilitating the services that cannot be provided via other means efficiently. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAV-BS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAV-BS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAV-BS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

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Multiple input single output aerial base station‐assisted networks: Joint 3D beam‐forming, aerial base station assignment, 3D placement, and radio resource allocation

Azizi

Mokari

2020

Trans Emerging Tel Tech

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In this article, we propose a novel method according to which joint aerial base station (ABS) assignment, three‐dimensional placement (3DP), three‐dimensional beam‐forming (3DB), and radio resource allocation are considered for multiple input single output (MISO) ABS‐based networks with orthogonal frequency division multiple access (OFDMA) technology. We aim to maximize the instantaneous data rate subject to some practical constraints. In light of the fact that our optimization problem is mixed‐integer nonlinear programming (MINLP) with high computational complexity, we propose a low complex and practical approach. Therefore, we resort to an alternate method by which the main optimization problem decomposes into three subproblems. Then we solve the resulting subproblems via linear approximation method, matching theory, and mesh adaptive direct search (MADS) algorithm. In order to shed light on our proposed approach, we decompose the proposed scenario into some cases with fewer optimization variables and compare them with our main problem. Our proposed approach boosts the instantaneous data rate compared to the decomposed cases with fewer optimization variables. Via simulation, we study the effect of various parameters like numbers of users, ABSs, and maximum transmission powers on the network performance. Finally, we analyze the network performance for suburban and urban environments considering various number of ABSs.

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Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations

Cited by 17 publications

References 43 publications

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization

Multiple input single output aerial base station‐assisted networks: Joint 3D beam‐forming, aerial base station assignment, 3D placement, and radio resource allocation

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