Service Constraints Research Articles

Green Rate‐Splitting Multiple Access for Multicell and Rate Fairness Maximization

ABSTRACTThis paper investigates the green design of rate‐splitting multiple access (RSMA) for multicell, multiantenna downlink communication systems to maximize rate fairness under quality of service (QoS) constraints. An optimization framework is developed to minimize the weighted sum power consumption by jointly optimizing transmit beamformers, power allocation, and common/private rate split while ensuring per‐user minimum rate requirements. The algorithm design explicitly considers the rate fairness among users via a newly proposed green fairness index. The nonconvex transceiver design problems are transformed into tractable forms and efficiently solved by applying tools from alternating optimization and successive convex approximation. Extensive simulations demonstrate that the proposed green RSMA optimization framework can effectively balance the throughput among cell‐edge and cell‐center users, hence combating the near‐far effect. Simulation results show that at 20‐dB SNR, green RSMA achieves over 20% and 50% sum rate improvement compared with NOMA and OMA, respectively. Moreover, green RSMA maintains high fairness among users, even when the channel conditions are disparate. When the weakest user is located 120 m from the base station, green RSMA exhibits a 21.2% higher fairness index than NOMA, showcasing its ability to ensure equitable service quality across users. The framework also achieves substantial energy efficiency gains, consuming less power while providing higher data rates than baseline schemes. These results demonstrate the effectiveness of green RSMA in enhancing spectral efficiency, user fairness, and energy efficiency in multicell, multiantenna communication systems, making it a promising solution for sustainable and high‐performance wireless networks.

Optimizing scientific workflow scheduling in cloud computing: a multi-level approach using whale optimization algorithm

Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing.

Waveform Design for the Integrated Sensing, Communication, and Simultaneous Wireless Information and Power Transfer System.

Next-generation communication systems demand the integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACSs) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS-based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF)-based target detection and parameter estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system's sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by an iterative method. Furthermore, to maximize the system's sensing performance under communication and PT quality of service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, which is solved using through the SDR technique. The simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), the waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and the beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS.

Fiber-optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications

Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes.

Mobility-aware SFC migration in dynamic 5G-Edge networks

The fifth-generation mobile networks bring an evolution in the development of novel classes of applications, increasing the demand for performance and flexibility to support new classes of services. Edge Computing and Network Function Virtualization are two key technologies that bring data processing closer to the user and allow the replacement of traditional hardware-based network functions with virtualized counterparts, which reduces costs and permits the provision of low-latency services. In this scenario, services are often implemented as a Service Function Chain (SFC), consisting of a set of ordered Virtual Network Functions (VNFs) that provide the required service functionality. During service execution, users can move from one location to another, which impacts service performance. An approach to reduce the impact of user mobility is to migrate services as users move to different places. However, the SFC migration problem imposes several challenges that require complex decision-making. VNFs should be migrated to nodes that have the necessary resources to execute the VNF instances. Also, the migration should be fast and effective, using as few resources as possible to reduce migration costs. In this paper, we propose a proactive and iterative SFC migration strategy to address the performance impact caused by user mobility. Our proposed solution anticipates the movement of users and estimates the impact of their mobility on service delay, proactively triggering service migrations when needed. The proposed algorithm decides which VNFs of the SFC should be migrated and which compute nodes will host the migrating VNFs, fulfilling service, resource, and migration constraints while maintaining a low migration overhead. Our algorithm also considers the dynamic nature of the edge environment in which other SFC placement and migrations might be occurring when an SFC is being migrated, which also introduces new challenges related to the availability and concurrency of resources required for migration.

Optimal location of base stations for cellular mobile network considering changes in users locations

In the past few decades, the demand for cellular networks has grown tremendously due to the growing demand for data. However, the vast majority of the users are dynamic since they use their smart phones and tablets to connect to these networks when they move to one location to another to attend sport games or other events for example. The location of these events might not cover the large demand. In this paper, we address the classical problem of locating base stations for a mobile cellular network to serve mobile users in a given geographical area considering the users' movements within the network. We developed a mixed integer programming model to provide the optimal location of base stations at different time periods with the network's minimum total cost (i.e., installation and operation of base stations). This paper considers the COST-231-Walfisch-Ikegami propagation model for the path loss calculation with a non-line-of-site situation. To demonstrate the capability of the proposed model, it is experimented with a real-world problem on the Red Sea coast of Saudi Arabia. The model was capable of finding the optimal base station locations with minimum installation and operational costs considering the capacity and quality of service constraints.

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

Increased rates of perinatal mental illness following COVID-19: the call for sufficient midwifery provision

The perinatal period is a known time of increased vulnerability to mental health illnesses, which are associated with significant morbidity and mortality. The COVID-19 pandemic saw rates of perinatal mental illness increase, remaining elevated ever since. In this article, postnatal depression is considered a specific perinatal mental health illness, which demonstrates the unique challenges in defining and diagnosing perinatal mental illness, and mitigating the long-term consequences to the infant. As public health practitioners, midwives are effective in preventing postnatal depression, yet may be limited in their ability to support women because of service constraints. Key drivers in the UK are mandating the parity of esteem of mental health and the improved provision of perinatal services, with the recruitment and retention of a sufficient midwifery service highlighted as priority.

Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

SummaryThe non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS.

What works, how and in which contexts when supporting parents to implement intensive speech and language therapy at home for children with speech sound disorder? A protocol for a realist review

IntroductionSpeech and language therapists (SLTs) worldwide report challenges with providing recommended, evidence-based intervention intensity for children with speech sound disorder (SSD). Challenges such as service constraints and/or family contexts impact...

Opportunities, challenges and implications of medical tourism development in Hong Kong

AbstractThis study revealed two different sets of push and pull factors affecting the medical travel decision process of two groups of mainland Chinese to Hong Kong, including potential and experienced medical tourists. The relations between identified factors and their willingness of personal visit and recommendation were also investigated. Regarding the recommendation intention, interpersonal acquisition and personal needs satisfaction are mostly considered by travelers in psychological level while recognized high medical reception capacity and quality are also prioritized in the pull aspect. However, local medical service constraints, perceived quality of medical experience shaped by healthcare suppliers, and destination attributes determine the two groups' responses to personal visit intention. Besides, the advantages of developing medical tourism in Hong Kong are implicated as can‐be‐perceived unique cultural attribute, mass tourism fame as medical tourism development basis, and reputation for high‐quality care formed with unintentional or deliberate promotion from third parties and various marketing channels.

20th Century Mortars: Physical and Mechanical Properties from Awarded Buildings in Lisbon (Portugal)—Studies towards Their Conservation and Repair

This paper addresses the study of renders and plasters’ physical and mechanical characteristics from selected buildings awarded during the 20th century with a renowned architectural prize in Lisbon, Portugal. The characterisation was done to understand mortars’ physical and mechanical properties and their evolution during the 20th century. These characteristics will also help determine compatibility requirements for future conservation and restoration interventions. Since these buildings have a heritage great interest status, the need to preserve them is a paramount issue. Fifty-three samples from nine case studies were studied via capillary water absorption, drying rates, open porosity, dynamic modulus of elasticity, and compressive strength. There were limitations in sample collection due to the buildings being in service and technical constraints regarding sample quantity for testing and separating layers of the multi-layer mortar system. Nevertheless, the results showed different ranges of quantitative values for these tests, whether the mortars were lime, gypsum, cement-based or had lime–cement blended formulations.

Optimal Service Migration for Data and Reasoning Fabric

In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.

Security-Reliability Tradeoff in RSMA-Based Communications Against Eavesdropper Collusion

This work exploits secure beamforming for achieving security-reliability tradeoff in rate splitting multiple access (RSMA)-based communications against eavesdropper collusion. Considering user fairness, we maximize the minimum secrecy rate (MSR) among all legitimate users subject to the quality of service (QoS) constraints and transmit power constraint. A feasibility check problem is first presented to avoid infeasible cases. Then, a successive convex approximation-based algorithm is proposed to iteratively optimize the beamforming design. Numerical results show that the proposed RSMA-based design outperforms the baseline design in terms of MSR within certain QoS requirement ranges to trade security for reliability by balancing the power allocated to the common stream and private streams. And the performance advantage enlarges as the number of collusive eavesdroppers increases.

Integration of NOMA With Reflecting Intelligent Surfaces: A Multi-Cell Optimization With SIC Decoding Errors

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.

Leveraging ride-hailing services for social good: Fleet optimal routing and system optimal pricing

With the penetration of ride-hailing services, the impacts of transportation network companies (TNC) on network performance grow. TNC comes with a ubiquitous sensing and pricing system that may be leveraged by public agencies to improve transportation system performance. This study first formulates and solves a mixed equilibrium (ME) of personal driving vehicles and ride-hailing vehicles, where TNCs centrally assign routes to ride-hailing vehicles to achieve fleet-wide optimum. We propose a novel fleet behavior desired by TNCs, named fleet-optimal behavior with service constraint (FOSC), which provides a good compromise between total fleet cost minimization and fairness among riders. However, we show that the system state of FOSC can be far from the system optimum state. To this end, we propose a novel Optimal Ride-hailing Pricing (ORHP) scheme for public agencies as an efficient manner to intervene ride-hailing online platforms. The essential idea of ORHP is to regulate and subsidize TNCs, in exchange for guaranteed network performance improvement. Under ORHP, public agencies set the value of a subsidy for each link for any TNC rider using this link. The subsidies are provided to TNCs, not directly to riders. TNC receives subsidies, and determines the best way to provide compensations for each rider who deviates from his/her ”shortest” route, determined by FOSC. TNC’s ultimate goal is to reduce their total cost, including total fleet vehicle travel time and total compensations, subtracted by the total subsidy received from public agencies. The ORHP is likely less controversial than other pricing schemes, since it calls for voluntary participation of travelers who are provided with multiple route options. Because it is built into the TNCs’ fare system, it is cost effective to implement and hard to game. This would be a win-win: a win for public agencies to cost-effectively improve system performance leveraging TNC’s platform without building physical infrastructure or services; and a win for TNC to profit from subsidies and improve service quality. ORHP is formulated as a bi-level optimization problem, solved with a sensitivity analysis based algorithm and tested on two networks implying the ORHP scheme can be effective: a small total subsidy provided to TNC can lead to significant improvement in system performance.

Electric vehicle charging network design with capacity and service considerations

Electric Vehicles (EVs) have a critical role in the future of sustainable transportation systems. Recently, improving the infrastructure for EVs has been a major focus of organizations around the world, such as local and federal governments, energy companies, and automotive original equipment manufacturers. In this article, we present a service network design framework to improve the infrastructure for EVs with quality of service constraints taken into consideration. We present a novel charging station location model with capacity allocation. The model maximizes the EV traffic flow captured and minimizes the average service time for drivers. The model formulation allows for assessing capacity and quality of service trade-offs, and operational insights are presented to reflect drivers’ expectations. We demonstrate an application for the solution approach on a real road network and evaluate the performance based on both computational efficiency and solution quality. Our results highlight how charging times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing waiting times. The value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider’s budget constraints.

Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning.

Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4-15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.

Examining Concordance Between Self-Report and Biomedical HIV Viral Load Data: A Scoping Review.

Assessment of HIV viral load based on laboratory results is the gold standard in HIV care and research. However, blood assay or accessing medical records is not always possible due to research or service contexts and constraints. Self-report of viral load test results expands data resources, is a convenient method of collecting data in both research and service settings, and is useful for HIV surveillance. The purpose of this scoping review was to identify existing literature on the validity of self-reported viral load data compared to blood assay or medical record review. We found that the existing literature is limited, with varied data collection methods, self-report measures, and study designs, as well as predictors of accuracy. Concordance between self-reported viral load and biomedical data varied across studies but appeared to be more consistent among samples recruited from clinical populations that reported engagement in HIV care. While it is difficult to draw definitive conclusions about the validity of self-reported viral load across existing studies, there is a need for a standardized measure and method of collection that can be utilized across diverse populations living with HIV.

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.