Multiuser Systems Research Articles

Intelligent Delay-Aware Partial Computing Task Offloading for Multiuser Industrial Internet of Things Through Edge Computing

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

A Phased Array Antenna with Novel Composite Right/Left-Handed (CRLH) Phase Shifters for Wi-Fi 6 Communication Systems

A linear phased array antenna excited with a novel composite right/left-handed (CRLH) phase shifters structure is proposed. The phase of the conventional CRLH transmission line is controlled with magnetically aligned micron-sized particles embedded inside the unit cell of the CRLH transmission line. The cascading of unit cells produces the desired phase shifts for the main beam scanning of the linear antenna array operating at a 5.5 GHz center frequency for Wi-Fi 6 applications. The proposed phase shifter design has a very low insertion loss (0.5–2 dB), excellent matching characteristics with the antenna array (less than −10 dB) and a small phase error (1–2 degrees). A 1 × 4 linear patch antenna phased array operating at a 5.5 GHz center frequency of the Wi-Fi 6 band is simulated using the Method of Moments (MoM) simulator platform. Then, the array is driven with the proposed novel CRLH phase shifters for the main beam at broadside and the main beam steered at 15- and 30-degree scan angles toward the desired users. For experimental validation, multiple unit cells of the proposed phase shifters are fabricated, and the 1 × 4 patch antenna array is fed with these fabricated unit cells of the phase shifters. The phased array radiation patterns are measured using an in-house fully calibrated anechoic chamber and were compared with simulated phased array patterns. The measured phased array patterns are in good agreement with the simulated patterns. As compared with commercially available phase shifters, the proposed novel CRLH phase shifters do not need external complex biasing circuitry, which is a major advantage in space constraint limitations at the router side of multi-user MIMO-OFDM systems.

QoS Guarantee Framework of English Literature Course Online System Based on Concurrent Optimization Network Environment

First, the initial connection isolation method ICS is established to separate false connections generated by malicious network behaviors from normal established connections, and form a controllable initial connection table. A strategy combining extended RSVP protocol and DiffServ model is designed to ensure the realization of QoS in large campus network. The simulation test results show that the application of this model can effectively improve the QoS of large-scale campus networks, and the problems that need to be paid attention to when running the parallel operation of the database management system correctly in the network environment. Especially to prevent data corruption and loss when developing multi-user systems.

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.

Channel Aging-Aware Precoding for RIS-Aided Multi-User Communications

Reconfigurable intelligent surfaces (RISs) have received intensive attention for their outstanding potential of reconstructing wireless propagation channels to significantly enhance the communication quality. In this article, we study the sum spectral efficiency (SE) of a multi-user communication system aided by an RIS. As opposed to previous works that assume independent and identically distributed block-fading channel models, we focus on a realistic scenario with the impact of channel aging due to user mobility and the spatial correlation among the reflecting elements of the RIS. Particularly, we first present a joint pragmatic model which encompasses the impact of channel estimation error and channel aging that facilitate the analytical derivations for the sum SE of the RIS-aided system. On this basis, we formulate the joint transmit beamforming and phase shift design as a nonconvex optimization problem to maximize the system sum SE. To address the design problem, we propose an efficient suboptimal channel aging-aware precoding (CAP) algorithm to obtain efficient suboptimal solutions for the considered problem capitalizing on the generalized fractional programming, the alternating direction method of multipliers method, and the alternating optimization method. Simulation results are provided to verify the effectiveness of the proposed algorithm and illustrate the impact of key system and channel parameters on the system performance. Our analysis unveils that a higher transmit power at the base station, a larger value of temporal correlation coefficients, a larger number and the size of RIS elements are beneficial to the system performance.

Robust Beamforming Design With Finite Blocklength for URLLC

Ultra-reliable and low-latency communications (URLLC) are essential for mission-critical applications, such as telemedicine, automatic drive, etc. In the design of URLLC, the robustness of multi-user beamforming transmission scheme is critical and challenging. To this end, we consider a robust beamforming design in the finite blocklength regime for a multi-user multi-antenna downlink system in the case of imperfect channel state information at the transmitter. A transmission power minimization problem is formulated to optimize the beamforming vectors under the premise of guaranteeing the requirements on transmission latency, reliability and the target user rates, which is non-convex. To solve it, we first reformulate the problem into a tractable form by using the convexity and concavity of asymptotic capacity in the finite blocklength regime, then adopt S-Procedure to convert the infinite constraints into finite ones. Finally, the Gaussian randomization method and the penalty function method are utilized to generate a feasible solution to the original optimization problem and to provide a good rank-one approximation, respectively. The simulation results confirm the robustness and effectiveness of the beamforming design.

Leakage Subspace Precoding and Scheduling for Physical Layer Security in Multi-User XL-MIMO Systems

We investigate the achievable secrecy spectral efficiency in a multi-user extra-large multiple-input multiple-output (XL-MIMO) system. In these beyond-5G scenarios, seen as the natural evolution of conventional massive MIMO systems, the distances from the mobile users to the base station become comparable to the antenna array dimensions. We show that the consideration of spherical-wavefront propagation inherent to these set-ups is beneficial for physical-layer security, as it provides immunity against eavesdroppers located in similar angular directions that would otherwise prevent secure communication under classical planar-wavefront propagation. A leakage subspace precoding strategy is also proposed for joint secure precoding and user scheduling, which allows to improve the secrecy spectral efficiency over a 40% compared to conventional zero-forcing methods, under different eavesdropper collusion strategies.

Метод назначения прав доступа к приложениям в корпоративной мобильной сети

The article focuses on studying and implementing a method of assigning access rights to the applications in a corporate mobile network with different requirements for security, which will allow considering the specific activities of multiple users. The existing solutions for assigning access rights to applications and services of the intranet are analyzed, the need to create a method that ensures information security when implementing access in corporate networks with different requirements for the level of security is substantiated. Due to the results of analysis of trends and development prospects of modern corporate mobile networks there has been found a contradiction between the requirements for information security of universal mobile devices with access to secure services and technical capabilities of information security systems that ensure access security in the corporate networks with different security requirements. To solve the problem, a multi-user system has been implemented that ensures operation of any computer equipment and mobile devices of an organization whose DBMS has a client-server architecture. The functional requirements for the developed method include the ability to perform user authorization, providing the administrator with the ability to manage user access rights to various applications, managing application storage, recording user operations, and keeping records. In the course of the software implementation of the method of assigning access rights, the design of the database is described in stages, a model of information flows is built, a physical diagram of the interaction of individual procedures is considered, on the basis of which the database is created, a user interface with forms that display information stored in the database is developed.

Information and sensing beamforming optimization for multi-user multi-target MIMO ISAC systems

Integrated sensing and communication (ISAC) has been envisioned as a key enabler in the next-generation wireless networks. In this paper, we consider the joint information and sensing beamforming design in a multi-user and multi-target multi-input multi-output ISAC system, where a transmit BS and a sensing BS collaborate to sense targets, and the transmit BS sends information streams to communication users at the same time. To optimize the sensing performance and guarantee the communication throughput, we formulate a joint beamforming design problem to minimize the trace of the weighted Cramer–Rao bound of target parameters subject to the sum-rate constraint. The problem is challenging to solve due to the intricate non-convex objective function and constraints. We firstly exploit the weighted mean square error minimization (WMMSE) and semidefinite relaxation (SDR) techniques to devise a WMMSE–SDR algorithm that can achieve a KKT point of the problem. The SDR can be shown to be tight for a subproblem in the WMMSE–SDR algorithm, which implies zero duality for the subproblem. Based on this property and fractional programming techniques, we further reformulate the beamforming problem as a min–max form with simple constraints which then can be efficiently solved by first-order min–max optimization algorithms. Finally, the proposed algorithms are evaluated extensively in simulations. Numerical results show that both proposed algorithms can achieve promising performance in sensing and communication, and the low-complexity algorithm has a significantly reduced computation time.

Joint hybrid-precoding design for MU-MISO systems with a subconnected architecture

In this study, we propose a joint hybrid-precoding algorithm for multiuser multiple-input single-output downlink systems. Specifically, we consider that the base station employs an energy-efficient hybrid-precoding subconnected (SC) architecture with fixed equal subarrays (FESA) (SC-FESA). Optimizing the analog precoding matrix in an SC-FESA architecture is challenging due to its unique constraint structure. In this study, to maximize system sum rate, we propose an efficient method to transform the system’s sum-rate optimization problem into a continuous and differentiable objective function wherein only the nonzero elements of the analog precoding matrix are optimized. For the formulated problem, we develop an alternating optimization (AO) approach to jointly optimize the digital and analog precoders in succession by maximizing the system’s sum rate. Specifically, in the proposed AO method, when the digital precoder is fixed, we employ the Riemannian conjugate gradient algorithm to generate the analog precoder. Furthermore, when the analog precoder is fixed, we use the minimum mean squared error method to obtain the digital precoder. Numerical simulation results show that the proposed AO algorithm improves the sum rate and energy efficiency of the SC-FESA architecture compared to existing algorithms.

Multi-user upper limb rehabilitation training system integrating social interaction

COVID-19 causes persistent symptoms such as weakness and myasthenia in most patients. Due to the cross-infection of COVID-19, the traditional face-to-face rehabilitation services are risky for the elderly. To ensure that the elderly in urgent need of rehabilitation services receive training while minimizing the disturbance of the COVID-19 pandemic on their social activities. We have improved the existing virtual upper limb training system, and added a social factor to the system. Seniors with upper limb rehabilitation needs can use the system to compete or collaborate with others for training. In addition, a set of natural and scientific exclusive gestures have been designed under the direction of following the doctor’s advice. The experiment is conducted jointly with the chief physicians of the geriatrics department in the authoritative class-A hospitals of Class III. Our experiment, which lasted for two months, showed that the virtual training system with social factors added had the best rehabilitation effect and enhanced the initiative of patients. The system has value for popularization during the COVID-19 epidemic.

Dual iterative algorithm for hybrid beamforming in mmWave downlink massive multi-user MIMO systems

Dual iterative algorithm for hybrid beamforming in mmWave downlink massive multi-user MIMO systems

USING SLURM WORKLOAD MANAGER FOR MANAGING SUPERCOMPUTERS AND LINUX CLUSTERS

The article is dedicated to the use of the Slurm Workload Manager for managing supercomputers and Linux clusters and highlighting the importance and advantages of using the Slurm Workload Manager in resource management on multi-user systems. Within the research, existing workload management systems and their limitations were analyzed. Based on this analysis, it was found that Slurm is one of the most widely used and efficient solutions in the field of resource management on multi-user systems. The article provides a detailed examination of the functionality of Slurm, including its core functions such as resource scheduling, task distribution, and system monitoring. Slurm allows users to efficiently utilize computational resources, distribute tasks among cluster nodes, ensure optimal CPU time usage, and control the system load. These features enable high productivity and efficiency in resource utilization. The advantages of Slurm compared to other resource management systems are presented in the article. Slurm is noted for its flexibility and the ability to configure various types of resources, as well as support for different scheduling algorithms. Limitations and challenges associated with using Slurm are also mentioned, providing readers with a comprehensive understanding of its capabilities and potential considerations for implementation. This article will provide readers with a detailed overview of the Slurm Workload Manager, its core functions, advantages, and limitations. With the comprehensive analysis and description of Slurm's core functions, the article will serve as an invaluable source of information for professionals working with large-scale computing clusters and supercomputers. It will also be beneficial for those interested in exploring best practices in resource management on multi-user systems and learning effective strategies for utilizing the Slurm Workload Manager.

Investigation of control characteristics in multi-user building heating system based on demand-side adjustment

Multi-user heating buildings based on demand-side adjustment have substantial potential in terms of energy savings and heat efficiency. The former heating system is generally controlled by considering the heating building as a whole. However, the original adjustment method is difficult to apply when there are several independent heat demand users in the building. In this background, this study established a hydrothermal coupled multi-level control model of a multi-user building heating system according to the actual situation of a multi-user heating building. In addition, it investigated the adjustment characteristics between user and building to effectively control the system. The results showed that the adjustment mode has a negligible effect on the room temperature and energy consumption for radiant floor heating systems and radiator heating systems. The control period had a large influence on the adjustment of the multi-user building heating system. The response time of the radiant floor heating system was over 4 h and that of the radiator system was less than 30 min. When the average indoor temperature reduced by 1 ℃, the energy-savings rate of the radiant floor heating system increased by 8.6 %, and that of the radiator heating system increased by 6.7 %. This study provides a basis for formulating and implementing control strategies in multi-user building heating systems.

Optimal Power Allocation With Multiple Joint Associations in Multi-User MIMO Full-Duplex Systems

Optimum power allocation is an effective way to mitigate residual self-interference and inter-user interference in multiple input multiple output full-duplex (FD) systems. However, current research mainly considers parts of influencing factors and sets service models fixed. Given this, we comprehensively focus on three perspectives in a novel power allocation method, which involve the muting management (MM) and the assignment of both base station antennas and subcarriers in the FD system. Then, we formulate an optimization problem to maximize the total spectrum efficiency. According to the categories of variables in the nonconvex objective function, we first propose a hierarchical algorithm, which is decomposed into the first-order Taylor approximation (FOTA) method and the greedy algorithm. The continuous and discrete variables related subproblems are solved through FOTA and greedy algorithm, respectively. Among them, the greedy algorithm is an alternative to a traditional method of exhaustive search. Considering the high complexity of the greedy algorithm, we further introduce deep reinforcement learning (DRL) instead to solve the corresponding subproblem. Thus, two Double Deep Q-learning Networks are constructed to train the samples in each sub-slot. Simulation results validate that the hybrid DRL-convex method outperforms the hybrid greedy-convex method. Meanwhile, the MM introduced scheme’s performance gain is more evident than that of the method without MM in many scenarios.

Anchor-Aided Channel Estimation for RIS-Assisted Multiuser Broadband Communications

Due to the passive nature of reconfigurable intelligent surface (RIS) and a large number of users, it is very challenging to estimate the cascaded base station (BS)-RIS-user channel directly, especially for multiuser broadband communication systems. In this letter, we propose a novel two-phase method to estimate multiuser broadband cascaded channels with the assistance of two anchor nodes. Specifically, in the first phase, the cascaded channel from the two anchors to the BS and that between two anchors are estimated to acquire the BS-RIS channel state information (CSI) with sign ambiguities. It is accomplished based on cyclic-prefixed single-carrier transmission and phase configuration at the RIS, which creates multiple cyclic delayed versions of transmitted signals for isolating all broadband channel taps. In the second phase, the RIS-user channel and subsequently the cascaded BS-RIS-user channel are estimated in the frequency domain using the knowledge of the partial BS-RIS CSI. Simulation results verify the superiority of the proposed scheme over the state-of-the-art methods in terms of estimation overhead, normalized mean-squared error and bit error rate.

Auxiliary Variable-Aided Hybrid Message Passing for Joint Channel, Phase Noise Estimation and Detection for Multi-User MIMO Systems

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system with independent phase noise (PN) at user terminals (UTs) due to non-synchronous noisy local oscillators. The conventional factor graph of MU-MIMO involves an observation factor with multiple high-dimensional variables in the form of multiplication and summation. In this work, the MU-MIMO factor graph is represented by introducing auxiliary variables, which enables the decomposition of the observation factor into several simpler sub-factors and then use different message passing techniques to obtain approximate marginals. Specifically, belief propagation and expectation propagation (BP-EP) are used for the linear factors for random walk process, modulation and coding, while mean field (MF) is used for non-linear factor in exponential form. To reduce the computational complexity, the non-Gaussian MF messages are approximated to be Gaussian by using the second order Taylor expansion at its belief obtained in the previous iteration. The proposed receiver has a quadratic computational complexity per symbol per iteration. Simulation results show that the performance of the proposed receiver is better than existing methods and is close to the matched filter bound (MFB) in terms of the bit error rate (BER).

Design of smart learning environment monitoring system based on multi-mode

The traditional smart campus pays attention to improving the management efficiency and neglects to improve the learning effect. This paper studies that the physical factors of smart learning environment affect students' learning effect, and designs a monitoring system of smart learning environment. The sensor is used to sense the temperature, humidity and air quality of the learning environment. The microcontroller system processes the sensing information of the sensor, and the server analyzes the data and generates the physical environment report and equipment control information to build a multi-mode multi-user smart learning environment monitoring system.

Quasi-Optimization of Resource Allocation and Positioning for Solar-Powered UAVs

Unmanned Aerial Vehicles (UAVs) will be an integral part of future smart cities to provide applications such as traffic management, environment monitoring and data collection. UAVs offer flexible deployment, dynamic mobility, and Ultra-Reliable and Low Latency Communications (URLLC). However, UAVs are power-hungry devices, and their limited battery capacity cannot support their flight and communication operations for a long period. Additionally, multi-carrier (MC) techniques will be vital for supporting futuristic multi-user communication systems. To overcome these issues, we propose a solar-powered UAV MC system to support URLLC services for multi-users. In this regard, we aim to maximize the system sum throughput and we jointly optimize UAV positioning and sub-carrier allocation. To solve the optimization problem, we propose the low-complexity coordinate descent approximation algorithm (CDAA). Lastly, we show the proposed algorithm converges quickly and simultaneously yields superior performance than fixed benchmark schemes for two simulated environments.

Sum-Rate Maximization and Leakage Minimization for Multi-User Cell-Free Massive MIMO Systems

Sum-Rate Maximization and Leakage Minimization for Multi-User Cell-Free Massive MIMO Systems