Service Requirements Of Users Research Articles

Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach.

In beyond fifth-generation networks, millimeter wave (mmWave) is considered a promising technology that can offer high data rates. However, due to inter-cell interference at cell boundaries, it is difficult to achieve a high signal-to-interference-plus-noise ratio (SINR) among users in an ultra-dense mmWave network environment (UDmN). In this paper, we solve this problem with the cooperative transmission technique to provide high SINR to users. Using coordinated multi-point transmission (CoMP) with the joint transmission (JT) strategy as a cooperation diversity technique can provide users with higher data rates through multiple desired signals. Nonetheless, cooperative transmissions between multiple base stations (BSs) lead to increased energy consumption. Therefore, we propose a multi-agent Q-learning-based power control scheme in UDmN. To satisfy the quality of service (QoS) requirements of users and decrease the energy consumption of networks, we define a reward function while considering the outage and energy efficiency of each BS. The results show that our scheme can achieve optimal transmission power and significantly improved network energy efficiency compared with conventional algorithms such as no transmit power control and random control. Additionally, we validate that leveraging channel state information to determine the participation of each BS in power control contributes to enhanced overall performance.

Health service requirements of mHealth app users: evidence from Hangzhou, China

PurposeChina’s healthcare system is being burdened by the increasing prevalence of chronic diseases. Therefore, this study investigated the health service requirements of mobile health applications (mHealth apps) users in Hangzhou, China. This study aimed to propose suggestions and theoretical references to improve mHealth apps and promote their development, thereby meeting public medical and health needs and creating an efficient medical service system.Design/methodology/approachWe constructed a model of health service demands using a literature review, network survey, and semi-structured interviews. We analyzed the demand attributes using the Kano model and Better-Worse index and obtained the priority ranking of demands.FindingsThe results revealed 25 demand elements in four dimensions: must-be (M), one-dimensional (O), attractive (A), and indifferent (I) requirements. The findings suggest that mHealth app developers can optimize health services by categorizing and managing health services, focusing on middle-aged users, enhancing the professionalism of health service providers, and improving the feedback mechanism.Originality/valueStudies on mHealth apps user demands, particularly on health service needs, remain scarce. This study employed a mixed-methods approach, integrating both qualitative and quantitative research techniques, to establish a priority ranking of user health service needs for mHealth apps. The study offers recommendations and theoretical references to optimize and improve mHealth app services.HighlightsConstruct a better health service requirements model for mHealth app users.Obtain the prioritization of demand elements in the model.Propose some management suggestions to improve mHealth apps.

Performance analysis of partial NOMA-based layered D2D communications

Conventionally, non-orthogonal multiple access (NOMA) has traditionally been implemented separately from orthogonal multiple access (OMA), aiming to improve the capacity of multi-user systems. However, a recent study has ventured beyond this conventional approach by integrating OMA and NOMA proportionally within the same system. In spite of these advancements, the consideration towards optimizing multi-user systems remains incomplete especially when user service requirements vary significantly. Therefore, this paper explores a novel layered device-to-device (D2D) partial NOMA (P-NOMA) scheme, which introduces a hybrid power-domain access method into multi-user systems. The analysis primarily focuses on evaluating both the system performance and the impact of various parameters on it. In contrast to conventional fully overlapped NOMA signals, P-NOMA signals are partially overlapped with an overlap rate that can be determined based on quality-of-service (QoS) requirements. Simulation results demonstrate that judicious utilization of P-NOMA can effectively enhance overall system performance, particularly in terms of sum rate (SR) metrics, while also flexibly accommodating diverse QoS requirements for multiple users.

Energy-efficient BS sleeping, user association, and resource allocation in full-duplex ultra-dense networks

Full-duplex ultra-dense network (FD-UDN) is a promising technology in 5G mobile networks for handling the increase in network capacity. However, interference management and energy efficiency are two of the most important challenges that must be addressed. With the small base stations (SBSs) densification, employing sleeping strategy coupled with resource management becomes an effective approach to manage interference and power consumption in FD-UDN. To the best of our knowledge, this aspect has not been addressed in previous work. To this end, we develop a framework to optimize the BS sleeping and resource management with the aim of maximizing energy efficiency and maintaining the quality of service (QoS) requirements of users. The problem is formulated as a non-convex mixed-integer non-linear programming problem, which is difficult to handle. Employing the Dinkelbach method, the objective function of the problem is converted to an equivalent parametric subtractive form. Then, the problem is decoupled into two sub-problems: user association and resource allocation, as well as BSs on/off switching. The former is solved using the iterative reweighted lq-norm minimization (IRM) method, and the latter is solved using the Lagrangian dual method and the constrained concave-convex procedure (CCCP). The simulation results demonstrate that the proposed method is more effective than traditional ones in simultaneously improving the EE, reducing power consumption, and keeping fewer SBSs active, especially in high network loads.

A Deadline-Budget Constrained Task Admission Control for a Software-as-a-Service Provider in Cloud Computing

Cloud computing is a pay-as-you-go model that provides users with on-demand access to services or computing resources. It is a challenging issue to maximize the service provider's profit and, on the other hand, meet the Quality of Service (QoS) requirements of users. Therefore, this paper proposes an admission control heuristic (ACH) approach that selects or rejects the requests based on budget, deadline, and penalty cost, i.e., those given by the user. Then a service level agreement (SLA) is created for each selected request. The proposed work uses Particle Swarm Optimization (PSO) and the Salp Swarm Algorithm (SSA) to schedule the selected requests under budget and deadline constraints. Performances of PSO and SSA with and without ACH are evaluated and compared using CloudSim. The simulation results prove that admission control maximizes profit by minimizing the number of task rejections and SLA violations. It also improves resource utilization while balancing makespan.

Joint Trajectory Design and Power Allocation for UAV Assisted Network With User Mobility

This paper investigates the unmanned aerial vehicle (UAV) assisted wireless communications where the UAV acts as the aerial base station to provide downlink data service to the ground users. We consider a typical hot-spot scenario in which the ground users move towards the target points of interests (PoIs) in groups. The UAV can also fly in air to cope with the movement of users. To maximize the system throughput, an optimization problem is formulated to jointly determine the subchannel assignment, the trajectory of UAV and its power allocation per time slot, where the navigation power of UAV, the co-channel interference as well as the quality of service (QoS) requirements of users are taken into account. As the problem is non-convex and hard to solve, we propose a cascade approach. First, we design a user clustering and pairing algorithm to accomplish the subchannel assignment. Then, we apply the block coordinate descent (BCD) based framework to decompose the remaining problem into the power allocation sub-problem and the trajectory design one. The sub-optimal solution can be gradually achieved through alternatively solving these two sub-problems. As for the power allocation sub-problem, we opt to the difference of convex (DC) functions form and the successive convex approximation (SCA) method to solve it. While for the trajectory design sub-problem, the auxiliary variables and SCA method are utilized. Vivid simulation results reveal that the proposed iterative algorithm can well satisfy the QoS of users and achieve significant performance gain compared with other benchmark schemes.

Time-Sensitive and Resource-Aware Concurrent Workflow Scheduling for Edge Computing Platforms Based on Deep Reinforcement Learning

The workflow scheduling on edge computing platforms in industrial scenarios aims to efficiently utilize the computing resources of edge platforms to meet user service requirements. Compared to ordinary task scheduling, tasks in workflow scheduling come with predecessor and successor constraints. The solutions to scheduling problems typically include traditional heuristic methods and modern deep reinforcement learning approaches. For heuristic methods, an increase in constraints complicates the design of scheduling rules, making it challenging to devise suitable algorithms. Additionally, whenever the environment undergoes updates, it necessitates the redesign of the scheduling algorithms. For existing deep reinforcement learning-based scheduling methods, there are often challenges related to training difficulty and computation time. The addition of constraints makes it challenging for neural networks to make decisions while satisfying those constraints. Furthermore, previous methods mainly relied on RNN and its variants to construct neural network models, lacking a computation time advantage. In response to these issues, this paper introduces a novel workflow scheduling method based on reinforcement learning, which utilizes neural networks for direct decision-making. On the one hand, this approach leverages deep reinforcement learning, eliminating the need for researchers to define complex scheduling rules. On the other hand, it separates the parsing of the workflow and constraint handling from the scheduling decisions, allowing the neural network model to focus on learning how to schedule without the necessity of learning how to handle workflow definitions and constraints among sub-tasks. The method optimizes resource utilization and response time, as its objectives and the network are trained using the PPO algorithm combined with Self-Critic, and the parameter transfer strategy is utilized to find the balance point for multi-objective optimization. Leveraging the advantages of reinforcement learning, the network can be trained and tested using randomly generated datasets. The experimental results indicate that the proposed method can generate different scheduling outcomes to meet various scenario requirements without modifying the neural network. Furthermore, when compared to other deep reinforcement learning methods, the proposed approach demonstrates certain advantages in scheduling performance and computation time.

Joint Resource Allocation and Drones Relay Selection for Large-Scale D2D Communication Underlaying Hybrid VLC/RF IoT Systems

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as a relay station, it is possible to avoid obstacles such as buildings and to communicate in a line-of-sight (LoS) environment, which naturally aligns with the requirement of VLC Systems. To further support the application of VLC in the IoT, subject to the challenges imposed by the constrained coverage, the lack of flexibility, poor reliability, and connectivity, drone relay-aided D2D communication appears on the horizon and can be cost-effectively deployed for the large-scale IoT. This paper proposes a joint resource allocation and drones relay selection scheme, aiming to maximize the D2D system sum rate while ensuring the quality of service (QoS) requirements for cellular users (CUs) and D2D users (DUs). First, we construct a two-phase coalitional game to tackle the resource allocation problem, which exploits the combination of VLC and RF, as well as incorporates a greedy strategy. After that, a distributed cooperative multi-agent reinforcement learning (MARL) algorithm, called WoLF policy hill-climbing (WoLF-PHC), is proposed to address the drones relay selection problem. Moreover, to further reduce the computational complexity, we propose a lightweight neighbor–agent-based WoLF-PHC algorithm, which only utilizes historical information of neighboring DUs. Finally, we provide an in-depth theoretical analysis of the proposed schemes in terms of complexity and signaling overhead. Simulation results illustrate that the proposed schemes can effectively improve the system performance in terms of the sum rate and outage probability with respect to other outstanding algorithms.

Intelligent Deployment of Delay-Sensitive Service Function Chain Based on Parallelization and Improved Cuckoo Search Algorithm

As one of the key focuses in 6G research, the space–air–ground integrated network incorporates a variety of technological frameworks. Network function virtualization allows network functions to be deployed on general servers in the form of software and creates a service function chain (SFC) according to user service requirements. In recent years, the deployment of SFC has become popular research due to the increasing demand for low delay in network application scenarios. Low delay is a crucial indicator of the quality of service, especially for delay-sensitive applications. To address this issue, we propose a method for the deployment of delay-sensitive SFC based on parallelization and the improved cuckoo search (ICS) algorithm (DDSSFC-PICS). This method optimizes the composition and deployment of SFC jointly. First, the serial structure of the SFC is transformed into a parallel structure by determining the dependency of virtual network functions, which reduces the length of the SFC and thereby reduces delay. Second, with the optimization goal of minimizing network delay, a parallel SFC deployment model is established under constraints including packet loss rate and resource availability. Finally, the ICS algorithm is applied for optimization, where delay is used as the fitness measure. By improving the Lévy flight step size and drawing inspiration from the whale algorithm, the performance of the cuckoo search (CS) algorithm is enhanced, leading to a further reduction in delay. The simulation results show that using the same CS deployment method, parallelized SFC has a significantly lower delay compared to serial SFC. Furthermore, the DDSSFC-PICS reduces the delay by 22.58% and 19.02%, respectively, compared with the CS deployment and particle swarm optimization SFC deployment methods.

Federated Reinforcement Learning-Based Resource Allocation in D2D-Enabled 6G

The current 5G and conceived 6G era with ultrahigh density, ultra-high frequency bandwidth, and ultra-low latency can support emerging applications like Extended Reality (XR), Vehicle to Everything (V2X), and massive Internet of Things (IoT). With the rapid growth of transmission rate requirements and link numbers in the wireless communication network, how to allocate resources reasonably and further improve spectrum utilization challenges the traditional approaches. To address these problems, technologies such as device-to-device (D2D) communication and machine learning (ML) are introduced to the traditional cellular communication network to improve network performance. However, due to the interference caused by spectrum reusing, efficient resource allocation for both cellular users and D2D users is necessary. In this article, we consider underlay mode D2D-enabled wireless network to improve the spectrum utilization, and deep reinforcement learning (DRL)- based federated learning (FL)-aided decentralized resource allocation approach to maximize the sum capacity and minimize the overall power consumption while guaranteeing the quality of service (QoS) requirement of both cellular users and D2D users. The performance of the proposed schemes is evaluated through simulations under 5G millimeter-wave (mm-wave) and 6G terahertz (THz) scenarios separately. The simulation results show that the proposal achieves significant network performance compared with the baseline algorithms.

Energy-Efficient Service-Aware Multi-Connectivity Scheduler for Uplink Multi-Layer Non-Terrestrial Networks

This paper introduces the concept of energy efficiency (EE) in the uplink with the capability of multi-connectivity (MC) in a multi-orbit non-terrestrial network (NTN), where user terminals (UTs) can be simultaneously served by more than one satellite to achieve higher peak throughput at reduced energy consumption. This concept also considers the service classification of the users, so that network dimensioning is performed in order to satisfy the quality of service (QoS) requirement of users. MC can increase throughput, but this entails increased power consumption at user terminal for uplink transmissions. To this end, an energy-efficient service-aware multi-connectivity (EE-SAMC) scheduling algorithm is developed in this paper to improve the EE of uplink communications. EE-SAMC uses available radio resources and propagation information to intelligently define a dynamic resource allocation pattern, that optimally routes traffic so as to reduce the energy consumption at the UT while ensuring QoS is maximized. EE-SAMC is designed based on the formulation of a non-convex combinatorial problem, it is solved in two ways involving firstly an optimization solution and secondly a heuristic approach. The effectiveness of EE-SAMC is compared with random allocation, round robin and heuristic schedulers in terms of EE, throughput and delay; EE-SAMC outperforms all schedulers.

UAV-Assisted Opportunistic Beamforming in Internet of Things Networks

Opportunistic beamforming (OBF) is a promising multiple-input-multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high performance and low-complexity channel estimation, OBF is appropriate for internet of things (IoT) networks.However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the quality of service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and spectrum efficiency.

Intelligent Resource Allocation in Backscatter-NOMA Networks: A Soft Actor Critic Framework

With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra massive machine-type communications networks are expected to connect large-scale internet of things (IoT) devices. However, due to NOMA co-channel interference, the power allocation to large-scale IoT devices becomes critical. The existing convex optimization-based solutions are highly complex, and therefore it is difficult to find the optimal solution to the resource allocation problem in a highly dynamic environment. To alleviate this problem, this work develops an efficient model-free BAC approach with a NOMA system to assist the base station with complex resource scheduling tasks in a dynamic BAC-NOMA IoT network. The objective is to increase the sum rate of uplink backscatter devices. More specifically, we jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely, the soft-actor critic (SAC) algorithm. With the advantage of entropy regularization, the SAC agent learns to explore and exploit the dynamic BAC-NOMA network efficiently. The proposed algorithm ensures the quality of service (QoS) requirements of downlink users while enhancing the sum rate of uplink backscatter devices. Numerical results reveal the superiority of the proposed algorithm over the conventional optimization (benchmark) approach in terms of the average sum rate of uplink backscatter devices. We show that the network with multiple downlink users obtained a higher reward with respect to a large number of iterations compared to episodes with a lower number of iterations. Moreover, the proposed algorithm outperforms the benchmark scheme and BAC with orthogonal multiple access in terms of the average sum rate with the different number of backscatter devices. Additionally, we show that our proposed algorithm enhances sum rate efficiency with respect to different self-interference coefficients and different noise levels. Finally, we evaluate and show the sum rate efficiency of the proposed algorithm with different QoS requirements and cell radii.

Joint Resource and Trajectory Optimization for Heterogeneous-UAVs Enabled Aerial-Ground Cooperative Computing Networks

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

User Selection and Power Allocation Scheme With SINR-Based Deep Learning for Downlink NOMA

In non-orthogonal multiple access (NOMA) systems, transmit power allocation plays a crucial role in maximizing the sum rate, while satisfying quality of service requirements of users. In prior study, an interior point method (IPM) has been presented as an iterative algorithm for power allocation, which can maximize the sum rate and minimize the outage rate. However, such iterative algorithms typically suffer from low computational efficiency and excessively high latency. To overcome these limitations, machine learning-based power allocation schemes have been widely studied. Along the same line, in this paper, focusing on unsupervised learning to improve the computational complexity for power allocation as well as user selection, we propose a deep neural network (DNN)-based joint user selection and power allocation technique for the cellular downlink multiple-input single-output(MISO) NOMA systems, where the transmit antenna selection and multi-cell cooperation are considered. Especially, an optimization problem for user selection and power allocation is formulated to maximize the sum rate under the constraint of minimum data-rate requirement. We propose a new loss function and training model for user selection and power allocation to solve the optimization problem, where the training and testing models are based on the signal-to-interference-plus-noise-ratio (SINR) information instead of channel state information, which can significantly reduce the input data size in DNN. Through simulations, we show that our proposed scheme provides comparable performances in terms of sum rate and outage rate to the optimal solutions, while requiring lower computational complexity compared to the optimal ones.

Deep Reinforcement Learning Algorithm for Latency-Oriented IIoT Resource Orchestration

Due to geographical factors and resource constraints, the traditional Internet architecture cannot meet the needs of the space–air–ground-integrated network (SAGIN) resource layout in the Industrial Internet of Things (IIoT) service. How to arrange network resources in SAGIN quickly and efficiently to meet the quality of service requirements of users has become a hot research topic in the industry. Based on the characteristics of SAGIN with multiple network segments, we convert the resource scheduling problem of SAGIN into a multidomain virtual network embedding (VNE) problem. This article proposes a latency-sensitive VNE algorithm based on deep reinforcement learning (DDRL-VNE) in the SAGIN environment. Unlike traditional latency optimization algorithms, we consider the effect of traffic size and hop count on latency when evaluating latency. We constructed a learning agent composed of a five-layer policy network and extracted a feature matrix as its training environment based on the network attributes of SAGIN. The node embedding is completed according to the probability that each node is embedded in the training, and then the breadth-first search strategy is used to complete the link embedding. The experimental results effectively illustrate the effectiveness of the algorithm in the SAGIN resource allocation problem.

Resource Allocation for Integrated Sensing and Communication in Digital Twin Enabled Internet of Vehicles

With the development of the sixth-generation (6 G) network, virtualization remains critical. The key to future virtualization lies in the service provisioning capability of the network and the service requirements of end users, which will lead to virtualization of the network and end users. Therefore, this paper proposes a holistic network virtualization architecture that integrates digital twin (DT) and network slicing to achieve the network management of service-centric and user-centric. With the explosive growth of latency-sensitive and computing-intensive in-vehicle applications, limited in-vehicle computing resources are difficult to meet diverse network requirements, and vehicle edge computing (VEC) has become a potential solution. However, computation offloading may face the dilemma of excessive upload traffic and unbearable upload time. Therefore, in order to minimize the overall response time (ORT) of the system, this paper proposes a new environment aware offloading mechanism (EAOM) based on the integrated sensing and communication system (ISAC) to solve the joint optimization problem of task scheduling and resource allocation. Considering the mobility of vehicles and the time-varying of environment, the optimization problem is modeled as a Markov decision process, and an improved algorithm combining Shapley-Q value and deep deterministic policy gradient (DDPG) is used to solve it. The simulation results indicate the effectiveness and superiority of the scheme proposed in our work.

Multi-cloud service provision based on decision tree and two-layer Restricted Monte Carlo Tree Search

Compared with traditional service provision methods, cloud computing has many advantages, such as on-demand services and flexible pricing systems, attracting widespread attention from both academia and industry. However, the abundance of cloud services makes it difficult for users to choose suitable ones. Furthermore, the cloud is usually a dynamically changing execution environment, requiring users to constantly switch service carriers for the best experience. Therefore, to improve the efficiency of service interactions in a multi-cloud environment and cope with the high concurrency demands, this paper proposes a multi-agent and distributed broker-based cloud service provisioning model. Equipped with a decision tree-based user preference learning module, brokers in the proposed model can continuously adjust their resource introduction strategy through training on historical data. In the process of cloud service interaction, brokers select the best cost-effective service for users within a limited time through a two-layer Restricted Monte Carlo Tree Search (RMTCS) algorithm. This paper develops a multi-cloud service interconnection system based on the Libcloud framework. Shielding the different interfaces of different cloud service providers in cloud virtual machine and storage services provides the most suitable services for users and realizes the unified cloud federation service supply. The experiment results prove that the proposed model achieves better performance over the traditional methods and can better meet the service requirements of users.

Cloud computing virtual machine consolidation based on stock trading forecast techniques

In Cloud Computing, the virtual machine scheduling in datacenters becomes challenging when trying to optimize user-service requirements and, at the same time, efficient resource management. Clumsy load management results in host overloads that trigger a continuous flow of virtual machine (VM) migrations to correct this situation, thus negatively impacting the Service Level Agreement (SLA), resource availability and energy consumption. The present paper explores the combined use of trend analysis techniques with time series forecasting techniques broadly used in stock markets, to improve VM-to-host consolidation. The main goal is to provide an efficient estimate of the near future trend of virtual machine resource usage and host availability. This information improves the scheduler’s decisions when determining the correct VM to be migrated and the candidate host to allocate it to. The results have demonstrated that it is possible to reduce the number of migrations by up to 75% while obtaining a reduction in the SLA violations by up to 60%. The results also showed noticeable improvements regarding the reduction of energy consumption. The migration decisions based on predictions of near-future resource usage trends using stock trading techniques showed a decrease in network usage, thus obtaining an energy saving of up to 16%.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.