Joint User Scheduling Research Articles

Joint User Scheduling and Phase Shift Optimization for STAR-RIS-Assisted Multicast Satellite Communications

Joint User Scheduling and Phase Shift Optimization for STAR-RIS-Assisted Multicast Satellite Communications

Outage Performance of MEC Systems With Joint User Scheduling and Access Point Selection

Outage Performance of MEC Systems With Joint User Scheduling and Access Point Selection

Joint User Scheduling, Trajectory, Beamforming and Power Allocation for Millimeter-Wave Full-Duplex UAV Relay

Joint User Scheduling, Trajectory, Beamforming and Power Allocation for Millimeter-Wave Full-Duplex UAV Relay

Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks

Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks

Joint User Scheduling, Power Allocation, and Trajectory Design for Joint Synthetic Aperture Radar and Communication UAV Systems

Joint User Scheduling, Power Allocation, and Trajectory Design for Joint Synthetic Aperture Radar and Communication UAV Systems

Joint User Scheduling and Precoding for RIS-Aided MU-MISO Systems: A MADRL Approach

Joint User Scheduling and Precoding for RIS-Aided MU-MISO Systems: A MADRL Approach

Storage-Aware Joint User Scheduling and Bandwidth Allocation for Federated Edge Learning

Storage-Aware Joint User Scheduling and Bandwidth Allocation for Federated Edge Learning

Joint User Scheduling, Base Station Clustering, and Beamforming Design Based on Deep Unfolding Technique

Joint User Scheduling, Base Station Clustering, and Beamforming Design Based on Deep Unfolding Technique

Joint User Scheduling and Precoding for XL-MIMO Systems With Imperfect CSI

We propose an algorithm for joint precoding and user scheduling in multiple-input multiple-output systems with extremely-large aperture arrays, assuming realistic channel conditions and imperfect channel estimates. The use of statistical channel state information (CSI) for user scheduling, and a proper selection of the set of users for which CSI is updated allow for obtaining an improved achievable sum spectral efficiency. We also confirm that the effect of imperfect CSI in the precoding vector design and the cost of training must be taken into consideration for realistic performance prediction.

Digital Twin and Meta RL Empowered Fast-Adaptation of Joint User Scheduling and Task Offloading for Mobile Industrial IoT

Digital Twin and Meta RL Empowered Fast-Adaptation of Joint User Scheduling and Task Offloading for Mobile Industrial IoT

Joint User Scheduling and UAV Trajectory Design on Completion Time Minimization for UAV-Aided Data Collection

We consider an unmanned aerial vehicle (UAV) assisting data collection from multiple sensor nodes (SNs). We provide a completion time minimization design via jointly deciding the UAV trajectory and the SN assignment scheme. In particular, we first characterize the fundamental features of the joint optimal solution to the formulated problem. On the one hand, the optimal UAV trajectory is proved following a successive-hover-fly (SHF) structure. Namely, in an optimal solution, the UAV successively visits multiple hovering points and performs hovering with designated duration, while the maximum speed is achieved during the whole flying period between each two hovering points. On the other hand, the optimal SN assignment is characterized to follow a segment-based scheme. Based on the two characterizations, we are motivated to implement SHF structure with turning points in trajectory design and reasonably assume each segment in SHF structure having constant SN assignment. Afterwards, we relax the binary constraints for SN assignments and establish a convex approximation for the reformulated problem, which enables an iterative algorithm. A suboptimal joint solution is obtained via iteratively optimizing the completion time. A realization strategy is also provided for the relaxed solution while assuring the completion of data collection tasks. Finally, the proposed solution is validated and evaluated through numerical results. Both a low complexity and an accurate task completion guarantee of our proposed solution are observed in comparison with the benchmarks.

QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels

Ensuring that the quality-of-service (QoS) requirements are satisfied in wireless communications systems with high user density is challenging due to the limitations on the transmit power budget and the number of resource blocks. In this paper, we propose a QoS-aware joint user scheduling and power allocation technique to enhance the number of served users in the downlink of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) with minimum QoS requirements guarantee. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints.We propose a generalized non-stationary multi-state channel model based on spherical wave propagation assuming that users under line-of-sight (LoS) and non-LoS (NLoS) transmission coexist in the same communication cell. This is done to accurately evaluate the proposed technique in realistic XL-MIMO scenarios. Numerical results reveal that the proposed CBS algorithm provides fair coverage over the whole cell area, achieving remarkable numbers of scheduled users with satisfied QoS requirements when users under the LoS and NLoS channel states coexist in the communication cell.

Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems

In multiuser communication systems, user scheduling and beamforming (US-BF) design are two fundamental problems that are usually studied separately in the existing literature. In this work, we focus on the joint US-BF design with the goal of maximizing the set cardinality of scheduled users, which is computationally challenging due to the non-convex objective function and the coupled constraints with discrete-continuous variables. To tackle these difficulties, a successive convex approximation based US-BF (SCA-USBF) optimization algorithm is firstly proposed. Then, inspired by wireless intelligent communication, a graph neural network based joint US-BF (J-USBF) learning algorithm is developed by combining the joint US and power allocation network model with the BF analytical solution. The effectiveness of SCA-USBF and J-USBF is verified by various numerical results, the latter achieves close performance and higher computational efficiency. Furthermore, the proposed J-USBF also enjoys the generalizability in dynamic wireless network scenarios.

Reinforcement Learning-Based Joint User Scheduling and Link Configuration in Millimeter-Wave Networks

In this paper, we develop algorithms for joint user scheduling and three types of mmWave link configuration: relay selection, codebook optimization, and beam tracking in millimeter wave (mmWave) networks. Our goal is to design an online controller that dynamically schedules users and configures their links to minimize system delay. To solve this complex scheduling problem, we model it as a dynamic decision-making process and develop two reinforcement learning-based solutions. The first solution is based on deep reinforcement learning (DRL), which leverages the proximal policy optimization to train a neural network-based solution. Due to the potential high sample complexity of DRL, we also propose an empirical multi-armed bandit (MAB)-based solution, which decomposes the decision-making process into a sequential of sub-actions and exploits classic maxweight scheduling and Thompson sampling to decide those sub-actions. Our evaluation of the proposed solutions confirms their effectiveness in providing acceptable system delay. It also shows that the DRL-based solution has better delay performance while the MAB-based solution has a faster training process.

Joint User Scheduling and Phase Shift Design for RIS Assisted Multi-Cell MISO Systems

This letter investigates the joint user scheduling and phase shift design for reconfigurable intelligent surface (RIS) assisted multi-cell downlink systems. A closed-form ergodic sum spectral efficiency (SE) approximation is utilized as the optimization metric. Based on this approximation, we schedule the users, whose cascaded channels are mostly correlated with each other’s, to maximize each user’s effective signal. Moreover, the RIS phase shift is designed to be the mean of the scheduled users’ cascaded channel phases. With the proposed transmission design, we find the optimal RIS deployment to achieve the highest maximum throughput which depends only on the relative locations of the BSs and RIS. In addition, we consider a more practical discrete RIS phase shift design based on a discrete Fourier transform (DFT) codebook. Simulation results show that the proposed low-complexity scheduling algorithm performs well.

Cross-Layer Optimization: Joint User Scheduling and Beamforming Design With QoS Support in Joint Transmission Networks

User scheduling and beamforming design are two crucial yet coupled topics for multiuser wireless communication systems. They are usually addressed separately with conventional optimization methods. In this paper, cross-layer optimization problem is considered, namely, the user scheduling and beamforming are jointly discussed, subjecting to the requirement of per-user quality of service and the maximum allowable transmit power for multicell multiuser joint transmission networks. To achieve the goal, a mixed discrete-continuous variables combinational optimization problem is investigated with aiming at maximizing the sum rate of the communication system. To circumvent the original non-convex problem with dynamic solution space, we first transform it into a 0-1 integer and continuous variables optimization problem, and then obtain a tractable form with continuous variables by exploiting the characteristics of the 0-1 integer constraints. Finally, the scheduled users and the optimized beamforming vectors are simultaneously calculated by an alternating optimization algorithm. We also theoretically prove that the base stations allocate zero power to the unscheduled users. Furthermore, two heuristic optimization algorithms are proposed respectively based on brute-force search and greedy search. Numerical results validate the effectiveness of our proposed methods, and the optimization approach gets relatively balanced results compared with the other two approaches.

A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems

The rapid development of mobile networks proliferates the demands of high data rate, low latency, and high-reliability applications for the fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the massive multiple-input-multiple-output (MIMO) technology is essential to realize the vision and requires coordination with resource management functions for high user experiences. Though conventional cross-layer adaptation algorithms have been developed to schedule and allocate network resources, the complexity of resulting rules is high with diverse quality of service (QoS) requirements and B5G features. In this work, we consider a joint user scheduling, antenna allocation, and precoding problem in a massive MIMO system. Instead of directly assigning resources, such as the number of antennas, the allocation process is transformed into a deep reinforcement learning (DRL) based dynamic algorithm selection problem for efficient Markov decision process (MDP) modeling and policy training. Specifically, the proposed utility function integrates QoS requirements and constraints toward a long-term system-wide objective that matches the MDP return. The componentized action structure with action embedding further incorporates the resource management process into the model. Simulations show 7.2% and 12.5% more satisfied users against static algorithm selection and related works under demanding scenarios.

User Scheduling and Power Allocation for Precoded Multi-Beam High Throughput Satellite Systems With Individual Quality of Service Constraints

For extensive coverage areas, multi-beam high throughput satellite (HTS) communication is a promising technology that plays a crucial role in delivering broadband services to many users with diverse Quality of Service (QoS) requirements. This article focuses on multi-beam HTS systems where all beams reuse the same spectrum. In particular, we propose a novel user scheduling and power allocation design capable of providing guarantees in terms of the individual QoS requirements while maximizing the system throughput under a limited power budget. Precoding is employed in the forward link to mitigate mutual interference among the users in multiple-access scenarios over different coherence time intervals. The combinatorial optimization structure from user scheduling requires an extremely high cost to obtain the global optimum even when a reduced number of users fit into a time slot. Therefore, we propose a heuristic algorithm yielding a good trade-off between performance and computational complexity, applicable to a static operation framework of geostationary (GEO) satellite networks. Although the power allocation optimization is signomial programming, non-convex on a standard form, the solution can be lower bounded by the global optimum of a geometric program with a hidden convex structure. A local solution to the joint user scheduling and power allocation problem is consequently obtained by a successive optimization approach. Numerical results demonstrate the effectiveness of our algorithms on GEO satellite networks by providing better QoS satisfaction combined with outstanding overall system throughput.

Joint User Scheduling, Power Configuration and Trajectory Planning Strategy for UAV-Aided WSNs

The future sixth-generation (6G) communication system is promising to provide differentiated communication services for massive users worldwide. To achieve this goal, wireless sensor networks (WSNs), regarded as the critical component of the 6G system, will be widely deployed in large-scale non-urban areas such as mountain villages, forests, and oceans. To ensure the high efficiency and stability of WSNs without the support of ground facilities, unmanned aerial vehicles (UAVs) are dispatched to provide assistance for WSNs. Nevertheless, considering the limitations of both the mobility and communication capability of UAVs, the diversified communication demands pose huge challenges for UAV-aided WSNs in large-scale application scenarios, such as how to schedule the SNs served in each time slot, how to optimize their working parameters, and how to plan the UAV trajectory. Therefore, a joint user scheduling, power configuration, and trajectory planning strategy is proposed in this article, which is formulated as an optimization problem suffering from non-convexity and complication. The successive optimization scheme is adopted here to efficiently solve this problem, which divides the original problem into three sub-problems and solves them iteratively. The simulation results prove the effectiveness and efficiency of the proposed strategy.

Joint User Scheduling and Hybrid Beamforming Design for Massive MIMO LEO Satellite Multigroup Multicast Communication Systems.

In the satellite multigroup multicast communication systems based on the DVB-S2X standard, due to the limitation of the DVB-S2X frame structure, user scheduling and beamforming design have become the focus of academic research. In this work, we take the massive multi-input multi-output (MIMO) low earth orbit (LEO) satellite communication system adopting the DVB-S2X standard as the research scenario, and the LEO satellite adopts a uniform planar array (UPA) based on the fully connected hybrid structure. We focus on the coupling design of user scheduling and beamforming; meanwhile, the scheme design takes the influence of residual Doppler shift and phase disturbance on channel errors into account. Under the constraints of total transmission power and quality of service (QoS), we study the robust joint user scheduling and hybrid beamforming design aimed at maximizing the energy efficiency (EE). For this problem, we first adopt the hierarchical clustering algorithm to group users. Then, the semidefinite programming (SDP) algorithm and the concave convex process (CCCP) framework are applied to tackle the optimization of user scheduling and hybrid beamforming design. To handle the rank-one matrix constraint, the penalty iteration algorithm is proposed. To balance the performance and complexity of the algorithm, the user preselected step is added before joint design. Finally, to obtain the digital beamforming matrix and the analog beamforming matrix in a hybrid beamformer, the alternative optimization algorithm based on the majorization-minimization framework (MM-AltOpt) is proposed. Numerical simulation results show that the EE of the proposed joint user scheduling and beamforming design algorithm is higher than that of the traditional decoupling design algorithms.