User Association Research Articles

Reconfigurable Intelligent Surface-Assisted Millimeter Wave Networks: Cell Association and Coverage Analysis

Reconfigurable intelligent surface (RIS) is emerging as a promising technology to achieve coverage enhancement. This paper develops a tractable analytical framework based on stochastic geometry for performance analysis of RIS-assisted millimeter wave networks. Based on the framework, a two-step cell association criterion is proposed, and the analytical expressions of the user association probability and the coverage probability in general scenarios are derived. In addition, the closed-form expressions of the two performance metrics in special cases are also provided. The simulation results verify the accuracy of the theoretically derived analytical expressions, and reveal the superiority of deploying RISs in millimeter wave networks and the effectiveness of the proposed cell association scheme to improve coverage. Furthermore, the effects of the RIS parameters and the BS density on coverage performance are also investigated.

DQN-GNN-Based User Association Approach for Wireless Networks

In the realm of advanced mobile networks, such as the fifth generation (5G) and beyond, the increasing complexity and proliferation of devices and unique applications present a substantial challenge for User Association (UA) in wireless systems. The problem of UA in wireless networks is multifaceted and requires comprehensive exploration. This paper presents a pioneering approach to the issue, integrating a Deep Q-Network (DQN) with a Graph Neural Network (GNN) to enhance user-base station association in wireless networks. This novel approach surpasses recent methodologies, including Q-learning and max average techniques, in terms of average rewards, returns, and success rate. This superiority is attributed to its capacity to encapsulate intricate relationships and spatial dependencies among users and base stations in wireless systems. The proposed methodology achieves a success rate of 95.2%, outperforming other methodologies by a margin of up to 5.9%.

Distribution of Benefits and Risks in Inter-Basin Water Transfers: The Case Study of NCT I from Upper Tana Basin to Nairobi City

Inter-Basin Water Transfers (IBWTs) introduce complex socioeconomic, hydrological, ecological, and institutional impacts that are instrumental to decision-making. However, since most studies focus on the hydrological and ecological aspects of IBWTs for the development of sustainable water resources, it is necessary to investigate the distribution of risks (costs) and benefits of IBWTs among stakeholders. This study aimed to identify and categorize stakeholders, their relationships, and the distribution of risks/costs and benefits in the Northern Collector Tunnel phase I (NCT 1) IBWT using a stakeholder analysis method. The Athi Water Works Development Agency (AWWDA) and the Ministry of Water and Irrigation (MoWI) played a central role in the project. However, most stakeholders found the devolved units Water Services Providers (WSPs), Water Resources Authority (WRA), Water Resources Users Associations (WRUAs), and Murang'a County as the most critical institutions. Public participation in NCT1 was not inclusive but only done to meet the statutory project requirements. Significant risks of the project emerged, such as reduced river flows, drying of the springs, and Murang'a residents' ability to meet their water needs. Therefore, it is necessary to compensate for diverted water through a water levy to cover environmental externalities and catchment conservation. The increase in water supply for Nairobi City was perceived as the main benefit for the recipient basin, but an improved water levy with adequate cross-subsidies was pointed out as an instrument to catalyze water savings by the urban water utility.

Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning

Small cells are a promising technique to improve the capacity and throughput of future wireless networks. However, user association and power allocation in heterogeneous networks is complicated by the dense deployment of small cells, resulting in non-convex and combinatorial problems. Conventionally, machine learning techniques are applied to the joint optimization problem, which has different action spaces. Gauging the continuous spaces to discrete spaces results in the loss of granularity due to discretization (e.g. potential power values in power allocation). Due to its hybrid action space, it is sub-optimal to solve joint user association (discrete spaces) and power allocation (continuous spaces) problems by applying traditional machine learning approaches. This work proposes a Multi-Agent Parameterized Deep Reinforcement Learning (MA-PDRL) approach to address the joint user association and power allocation problem efficiently. According to simulation results, the proposed multi-agent PDRL performs better in energy efficiency and QoS satisfaction than WMMSE, game theory, Q-learning, and DRL techniques.

Building an empowerment program to improve the health of patients with severe mental disorders

Risk factors and cardiovascular diseases are overrepresented in people with severe and persistent mental disorders. A person diagnosed with schizophrenia or bipolar disorder is two to three times more likely to die of cardiovascular disease than the general population. An empowerment program has been co-created to reduce these health inequalities. It is one part of the COPsyCAT project. The people-centered approach has been used. The participation of the patients, caregivers, and health professionals was decisive. Stakeholders redefined the objectives of the program. The aim is to improve quality of life, rather than reducing cardiovascular risk. Existing tools -that have been evaluated for their usability - were selected to allow for self-directed patient orientation, so that the constraints between psychiatry and primary care could be circumvented. The program is based on the pooling of existing resources in a territory. The individual power of action and the organization of healthy offers are thus designed to reinforce each other. This article concretely describes the steps through to which the COPsyCAT empowerment program was designed, in co-construction by the researchers of the study, the users and user associations and healthcare professionals at based on their experiential knowledge. The feasibility of the program and the appropriation of tools in real situations will soon be evaluated. The measure of the program’s effectiveness on cardiovascular risk will come in second time.

Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.

Multiple Aerial Base Station Deployment and User Association Based on Binary Radio Map

The utilization of the aerial base station (ABS) has been treated as a promising solution to the coverage, deployment and cost savings problems in the wireless networks. Different from the terrestrial networks, in the ABS assisted air-to-ground network, the service positions of ABSs and user association should be jointly optimized, which is challenging due to the unknown propagation environment in the task area. In this paper, a binary radio map (BRM) is constructed to grant the ABSs the location-specific channel knowledge within the entire task area. With the assistance of the BRM, a multiple ABS deployment and user association framework is proposed. The framework consists of an offline optimization stage during which the initial ABS deployment position and user association are jointly optimized based on the BRM, and an online refinement stage during which the ABS positions and user association are refined in real time according to both the observed quality of service (QoS) and virtual QoS emulated by the BRM. The performance of the proposed framework is examined in a complex urban scenario with mobile users. The results show that higher achievable rate performance and lower on-board energy consumption can be realized when compared with the reference schemes.

Joint Optimization of 3D Placement and Radio Resource Allocation for Per-UAV Sum Rate Maximization

Unmanned aerial vehicles (UAV) have emerged as a practical solution that provides on-demand services to users in areas where the terrestrial network is non-existent or temporarily unavailable, e.g., due to natural disasters or network congestion. In general, UAVs' user-serving capacity is typically constrained by their limited battery life and the finite communication resources that highly impact their performance. This work considers the orthogonal frequency division multiple access (OFDMA) enabled multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive an efficient technique for the allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: (i) 3D placement and user association and (ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after fewer than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, results showed that the optimal power and sub-carrier allocation help to mitigate the inter-cell interference that directly impacts the system's performance.

GNN-Based Power Allocation and User Association in Digital Twin Network for the Terahertz Band

GNN-Based Power Allocation and User Association in Digital Twin Network for the Terahertz Band

Mobile Edge Computing (MEC)-Enabled UAV Placement and Computation Efficiency Maximization in Disaster Scenario

Unmanned aerial vehicles (UAVs) are considered for disaster management to provide better coverage or help first responders provide advanced services. Furthermore, UAVs combined with mobile edge computing (MEC) capabilities, have recently emerged as a support for cloud computing by deploying UAV-assisted MEC at the network edge to reduce the latency and load of cloud computing data centers. We investigate the problem of maximizing the utility function by jointly optimizing the computing power, user associations, performance, duration, and location of user equipments (UEs) in UAV-assisted MEC networks in a disaster scenario. We considered several constraints including delay, quality of service, and coverage. The optimization problem is a mixed-integer non-linear programming problem. We propose a multi-stage offloading algorithm based on a learning algorithm and an interior-point method to obtain a viable solution. The simulation results obtained demonstrate the effectiveness of our proposed algorithm compared to existing schemes.

Multi-Agent Deep Reinforcement Learning for Joint Decoupled User Association and Trajectory Design in Full-Duplex Multi-UAV Networks

Multi-Agent Deep Reinforcement Learning for Joint Decoupled User Association and Trajectory Design in Full-Duplex Multi-UAV Networks

A New Virtual Network Topology-Based Digital Twin for Spatial-Temporal Load-Balanced User Association in 6G HetNets

A New Virtual Network Topology-Based Digital Twin for Spatial-Temporal Load-Balanced User Association in 6G HetNets

Developmental role of water user associations in some villages in the Abnob and Al-Fath centers in Assiut Governorate

Developmental role of water user associations in some villages in the Abnob and Al-Fath centers in Assiut Governorate

Joint User Association and Deployment Optimization for Delay-Minimized UAV-Aided MEC Networks

Joint User Association and Deployment Optimization for Delay-Minimized UAV-Aided MEC Networks

Investigation on the Critical Densification Levels for Coupled and Decoupled User Association in Ultra-dense Networks

Investigation on the Critical Densification Levels for Coupled and Decoupled User Association in Ultra-dense Networks

Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks.

Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm.

Ruin Theory for User Association and Energy Optimization in Multi-Access Edge Computing

In this correspondence, a novel framework is proposed for analyzing data offloading in a multi-access edge computing system. Specifically, a two-phase algorithm, is proposed, including two key phases: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1) user association phase</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2) task offloading phase</i> . In the first phase, a ruin theory-based approach is developed to obtain the users association considering the users' transmission reliability and resource utilization efficiency. Meanwhile, in the second phase, an optimization-based algorithm is used to optimize the data offloading process. In particular, ruin theory is used to manage the user association phase, and a ruin probability-based preference profile is considered to control the priority of proposing users. Here, ruin probability is derived by the surplus buffer space of each edge node at each time slot. Giving the association results, an optimization problem is formulated to optimize the amount of offloaded data aiming at minimizing the energy consumption of users. Simulation results show that the developed solutions guarantee system reliability, association efficiency under a tolerable value of surplus buffer size, and minimize the total energy consumption of all users.

Energy-Efficiency Optimization for Multiple Access in NOMA-Enabled Space–Air–Ground Networks

Due to the flexible deployment of unmanned aerial vehicles (UAVs) and the wide-area coverage of satellites, the space-air-ground (SAG) communication network can provide flexible and pervasive connectivity, especially in remote areas. In this work, we investigate the uplink transmission in a SAG network, where the non-orthogonal multiple access mechanism is adopted at the UAVs to enhance the number of access from ground user equipments (UEs) and a low earth orbit satellite offers the wireless backhaul for UAVs. In particular, the energy efficiency (EE) of the considered network is maximized by optimizing the user association (UA), the power allocation (PA), and the UAV 3D trajectory jointly with the consideration of the movement of the satellite. To tackle the formulated problem, by leveraging the block coordinate descent (BCD) method, we develop a joint UA, PA, and UAV trajectory (namely, JUPT) optimization algorithm, i.e., the original problem is decomposed into three subproblems, and the subproblems are solved iteratively until convergence. Specifically, we propose to include the virtual UEs in the system and develop a low-complexity matching algorithm to effectively solve the UA problem. A successive convex approximation (SCA)-based Dinkelbach algorithm is then adopted to address the PA problem. Later, with the introduction of the auxiliary variables, the UAV 3D trajectory subproblem is iteratively solved by the SCA method. Our numerical results demonstrate the superiority of the proposed JUPT algorithm, which obtains significantly higher EE compared to the benchmark schemes. Moreover, the rapid convergence of the JUPT algorithm is verified.

QoS-Aware User Association and Transmission Scheduling for Millimeter-Wave Train-Ground Communications

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

MSM: Mobility-Aware Service Migration for Seamless Provision: A Data-Driven Approach

Mobile Edge Computing (MEC) is a promising approach to support high-quality time-sensitive applications. With the increasing number of mobile devices, achieving efficient service migration management has become non-trivial in MEC. In addition, the service migration issue is difficult to be solved in real-time due to user mobility and dynamic network conditions. In this paper, we investigate the mobility-aware service migration problem in MEC by introducing a data-driven framework. Firstly, service migration is formulated as an optimization problem for minimizing the long-term system delay that consists of computing, communication, and migration delays. Secondly, we propose a Mobility-aware Service Migration scheme, named MSM, consisting of three layers: 1) the data collection layer; 2) the association patterns analysis layer; and 3) the service migration layer. Specifically, we first collect users’ historical Wi-Fi traces to mine the association patterns. We then design a user management mechanism to reduce the complexity of decision-making by using user association patterns. Finally, we formulate the service migration as a 2D-Markov decision process and devise a deep reinforcement learning (DRL) based algorithm to obtain service migration decisions in a large-scale MEC scenario. Extensive data-driven experiments are conducted to demonstrate the efficacy of MSM in reducing the system delay.