Improve System Throughput Research Articles

Improving the System Performance in Terrestrial-Satellite Relay Networks by Configuring Aerial Relay

Driven by the massively growing applications in Internet of Things (IoT) and the new requirements of the six generation (6G) wireless network, the amount of terrestrial mobile devices (TMDs) and data traffic in wireless communications have increased explosively. Consequently, the integrated terrestrial-satellite relay networks (ITSRNs) have emerged to address the issue of the huge wireless traffic with limited allocated resources, which attracts extensive research attention to the field of IoT. Limited improvement of system throughput is realized by configuring terrestrial relays (TR) in the previous works. In this paper, by introducing an aerial relay (AR), we propose a novel structure of ITSRN and investigate the system throughput maximization and energy consumption minimization problems by jointly optimizing power allocation and AR altitude. Specifically, the system implementation includes two periods: satellite to relay (S-R) period and satellite to devices (S-D) period. To maximize the overall system throughput, we propose a joint optimization scheme of the power allocation and altitude of AR. Due to its non-convexity, a bilevel programming method is proposed to obtain the global optimal solution, where the closed-form expressions of power allocation and the optimal altitude of AR are derived. Subsequently, we investigate the energy consumption minimization problem during S-R period, which is formulated as a convex problem. We propose a low complexity algorithm and derive the optimal closed-form solution based on the rigorous mathematical analysis. The effectiveness of the proposed algorithms and solutions are verified by extensive simulations.

A Multi-Beam Satellite Cooperative Transmission Scheme Based on Resources Optimization and Packets Segmentation

Multi-beam satellite communication systems are promising architectures in the future. A packet is transmitted by multi-satellite and multi-beam cooperatively, which can provide efficient spectrum utilization, improve system throughput, and guarantee Quality of Services (QoS). In multi-beam satellite communication systems, multi-layer and multi-dimensional radio resources change dynamically, which leads to the discontinuity of optimal resources and the lack of mapping balance between packets and radio resources. To deal with these problems, we propose a cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm and an adaptive packet segmentation scheme based on the irregular gradient algorithm. The cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm simulates cross-layer and cross-dimension optimization for the high-dynamic and multi-dimensional radio resources by considering the channel state information (CSI) and QoS in the multi-beam satellite communication system. The optimal resources are taken as the weight of irregular gradient algorithm to segment packets and map packets to radio resources, which can realize the mapping balance between packets and radio resources and ensure the efficiency and reliability of communication. The simulations show that the new transmission scheme improves the normalized system throughput and user satisfaction index by 18.7% and 6.2%, respectively.

Research on PDMA system based on complementary sequence and low complexity detection algorithm

With the intensive deployment of mobile networks and the vigorous development of new multimedia services, video has gradually become the mainstream of cultural consumption. The contradiction between the proliferation of video data services and the scarcity of spectrum resources has brought great challenges to the current network resource allocation. Non-orthogonal multiple access (NOMA) can be used to solve this problem by signal superposition and spectrum multiplexing to improve system access capability. As a new type of joint optimization design of transmitter and receiver side, PDMA has high research value. In this paper, a framework of PDMA video transmission system based on H.264 video compression coding (HVC-PDMA) is proposed. Poly complementary sequence (PCS) spread spectrum coding is performed on the transmission codebook in order to improve the transmission accuracy. Meanwhile, a low complexity serial sphere compensated Max-log MPA (SSCM-MPA) algorithm is proposed to reduce the complexity of the multi-user detection algorithm. Simulation results show that the PCS spread spectrum can improve system throughput and peak signal-to-noise ratio (PSNR) while reducing bit error rate (BER). SSCM-MPA algorithm can greatly reduce the complexity and improve the transmission efficiency.

MIMO broadcast scheduling using binary spider monkey optimization algorithm

SummaryMulti‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results.

Hardware Topologies for Decentralized Large-Scale MIMO Detection Using Newton Method

Centralized Massive Multiple Input Multiple Output (MIMO) uplink detection techniques for baseband processing possess severe bottleneck in terms of interconnect bandwidth and computational complexity. This problem has been addressed in the current work by adapting the centralized Newton method for decentralized MIMO uplink detection leveraging several Base Station antenna clusters. The proposed decentralized Newton (DN) method achieves error-rate performance close to centralized Zero Forcing detector as compared to other decentralized techniques. Two hardware topologies, namely the ring and the star topologies, are proposed to assess and discuss the trade-off among interconnect bandwidth and throughput, in comparison with contemporary decentralized MIMO uplink detection techniques. As such the following findings are elaborated. On BS antenna cluster scaling for different MIMO system configurations, the ring topology provides high throughput at constant interconnect bandwidth, while the star topology provides lower latency with a deterministic variation in the hardware resource consumption. Due to strategic optimizations on the hardware implementation, additional user equipment can be allotted at a fractional increase in Field Programmable Gate Array resource consumption, improved energy efficiency, and increased transaction of bits per Joule. The ring topology can process additional subcarrier at a fractional increase in latency and improved system throughput.

Help from space: grant-free massive access for satellite-based IoT in the 6G era

The Sixth-Generation (6G) standard for wireless communications is expected to realize ubiquitous coverage for massive Internet of Things (IoT) networks by 2030. Satellite-based communications are recognized as a highly promising technical enabler to satisfy IoT service requirements in the 6G era. This study analyzes multiple access technologies, which are essential for the effective deployment of satellite-based IoT. First, we thoroughly investigate the existing research related to massive access, including information-theory considerations as well as Non-Orthogonal Multiple Access (NOMA) and Random Access (RA) technologies.Then, we explore the influence of the satellite transmission environment on multiple access technologies. Based on this study, a Non-orthogonal Massive Grant-Free Access (NoMaGFA) scheme, which reaps the joint benefits of RA and NOMA, is proposed for asynchronous transmissions in satellite-based IoT to achieve improved system throughput and enhance the system robustness under varying traffics. Finally, we identify some important and interesting future developments for satellite-based IoT, including waveform design, transceiver design, resource allocation, and artificial intelligence-enhanced design.

Intelligent Network Selection Algorithm for Multiservice Users in 5G Heterogeneous Network System: Nash Q-Learning Method

The 5G heterogeneous network architecture integrates different radio access technologies (RATs), which will support the large-scale communication connection of massive Internet-of-Things (IoT) devices. However, as the rapid growth of IoT connections, personalized requirements of services requested and heterogeneity deepening of the network system, how to design an intelligent network selection scheme for user devices (UDs) is becoming a crucial challenge in the 5G heterogeneous network system. Most of the existing network selection methods only optimize the selection strategies from the user side or network side, which results in heavy network congestion, poor user experience, and system performance degradation. Accordingly, we propose a multiagent <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 network selection (MAQNS) algorithm based on Nash <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, which can learn a joint optimal selection strategy to improve system throughput and reduce user blocking on the premise of ensuring the requirements of IoT services. In particular, we apply the discrete-time Markov chains to model the network selection, and the analytic hierarchy process (AHP) and gray relation analysis (GRA) are jointly utilized to obtain user preferences for each network. Finally, performance evaluation demonstrates that comparing to the existing schemes, MAQNS proposed cannot only improve system throughput and reduce user blocking but also promote user experience on average energy efficiency and delay.

Performance Modeling Analysis of D-MSMR-CARQ with Relay Selection in Wireless Sensor Networks

Reliable and efficient real-time transmission is an important and challenging issue for wireless sensor networks (WSNs). Truncated retransmission times and relay selection can effectively reduce transmission delay and improve system throughput. A new direct multisource multirelay cooperative automatic repeat request (D-MSMR-CARQ) protocol based on truncation with two relay selection methods in WSNs is analytically analyzed in this paper. Firstly, based on two different relay selection methods under the maximum ratio combining (MRC), the discrete time Markov chain (DTMC) model of D-MSMR-CARQ protocol and state space is established. Secondly, for each D-MSMR-CARQ protocol based on different relay selection method, we obtain the closed-form expressions of the system average transmission delay and the expressions of the system throughput through state transition probabilities. Finally, numerical results reveal that the first relay selection method outperforms the second relay selection method on the average transmission delay performance for the proposed protocol. More specifically, the delay performance of the proposed protocol can be improved by 13% compared with the nondirect-link protocol when the channel environment is the same; the proposed protocol improves the throughput performance by 47% compared with the nondirect protocol when the channel environment is harsh under the same simulation parameters. Furthermore, the optimal number of source nodes and relay nodes is determined.

Employing Vertical Elasticity for Efficient Big Data Processing in Container-Based Cloud Environments

Recently, “Big Data” platform technologies have become crucial for distributed processing of diverse unstructured or semi-structured data as the amount of data generated increases rapidly. In order to effectively manage these Big Data, Cloud Computing has been playing an important role by providing scalable data storage and computing resources for competitive and economical Big Data processing. Accordingly, server virtualization technologies that are the cornerstone of Cloud Computing have attracted a lot of research interests. However, conventional hypervisor-based virtualization can cause performance degradation problems due to its heavily loaded guest operating systems and rigid resource allocations. On the other hand, container-based virtualization technology can provide the same level of service faster with a lightweight capacity by effectively eliminating the guest OS layers. In addition, container-based virtualization enables efficient cloud resource management by dynamically adjusting the allocated computing resources (e.g., CPU and memory) during the runtime through “Vertical Elasticity”. In this paper, we present our practice and experience of employing an adaptive resource utilization scheme for Big Data workloads in container-based cloud environments by leveraging the vertical elasticity of Docker, a representative container-based virtualization technique. We perform extensive experiments running several Big Data workloads on representative Big Data platforms: Apache Hadoop and Spark. During the workload executions, our adaptive resource utilization scheme periodically monitors the resource usage patterns of running containers and dynamically adjusts allocated computing resources that could result in substantial improvements in the overall system throughput.

Achieving high throughput and elasticity in a larger-than-memory store

Millions of sensors, mobile applications and machines now generate billions of events. Specialized many-core key-value stores (KVSs) can ingest and index these events at high rates (over 100 Mops/s on one machine) if events are generated on the same machine; however, to be practical and cost-effective they must ingest events over the network and scale across cloud resources elastically. We present Shadowfax, a new distributed KVS based on FASTER, that transparently spans DRAM, SSDs, and cloud blob storage while serving 130 Mops/s/VM over commodity Azure VMs using conventional Linux TCP. Beyond high single-VM performance, Shadowfax uses a unique approach to distributed reconfiguration that avoids any server-side key ownership checks or cross-core coordination both during normal operation and migration. Hence, Shadowfax can shift load in 17 s to improve system throughput by 10 Mops/s with little disruption. Compared to the state-of-the-art, it has 8x better throughput (than Seastar+memcached) and avoids costly I/O to move cold data during migration. On 12 machines, Shadowfax retains its high throughput to perform 930 Mops/s, which, to the best of our knowledge, is the highest reported throughput for a distributed KVS used for large-scale data ingestion and indexing.

Fast tag identification for mobile RFID robots in manufacturing environments

Purpose In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection. Design/methodology/approach A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection. Findings The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission. Originality/value In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

Dynamic Set Planning for Coordinated Multi-Point in B4G/5G Networks.

Coordinated Multi-Point (CoMP) is an important technique in B4G/5G networks. With CoMP, multiple base stations can be clustered to compose a cooperating set to improve system throughput, especially for the users in cell edges. Existed studies have discussed how to mitigate overloading scenarios and enhance system throughput with CoMP statically. However, static cooperation fixes the set size and neglects the fast-changing of B4G/5G networks. Thus, this paper provides a full study of off-peak hours and overloading scenarios. During off-peak hours, we propose to reduce BSs’ transmission power and use the free radio resource to save energy while guaranteeing users’ QoS. In addition, if large-scale activities happen with crowds gathering or in peak hours, we dynamically compose the cooperating set based on instant traffic requests to adjust base stations’ BSs’ transmission power; thus, the system will efficiently offload the traffic to the member cells which have available radio resources in the cooperating set. Experimental results show that the proposed schemes enhance system throughput, radio resource utilization, and energy efficiency, compared to other existing schemes.

D2D Assisted Cellular Networks in Licensed and Unlicensed Spectrum: Matching-Iteration-Based Joint User Access and Resource Allocation

Device-to-Device (D2D) communications, which enable direct communication between nearby user devices over the licensed spectrum, have been considered a key technique to improve spectral efficiency and system throughput in cellular networks (CNs). However, the limited spectrum resources cannot be sufficient to support more cellular users (CUs) and D2D users to meet the growth of the traffic data in future wireless networks. Therefore, Long-Term Evolution-Unlicensed (LTE-U) and D2D-Unlicensed (D2D-U) technologies have been proposed to further enhance system capacity by extending the CUs and D2D users on the unlicensed spectrum for communications. In this paper, we consider an LTE network where the CUs and D2D users are allowed to share the unlicensed spectrum with Wi-Fi users. To maximize the sum rate of all users while guaranteeing each user’s quality of service (QoS), we jointly consider user access and resource allocation. To tackle the formulated problem, we propose a matching-iteration-based joint user access and resource allocation algorithm. Simulation results show that the proposed algorithm can significantly improve system throughput compared to the other benchmark algorithms.

A Power Control Scheme to Exploit Capture Effect with Fairness Consideration in WLAN

In this paper, we develop a physical/medium-access-control cross layer design to improve system throughput with the consideration of fairness for IEEE 802.11 WLAN. From PHY layer perspective, when an access collision occurs, the access point can still decode the corresponding data successfully if the received signal to interference plus noise ratio is larger than the threshold. This phenomenon is referred to as the capture effect. To improve system throughput, this work proposes a Differential Reception-Power Power Control scheme to take advantage of the capture effect. However, the proposed power control scheme cannot provide a fair transmission environment even though it improves the system throughput. To resolve this problem, this work proposes two methods: the adjustment of contention window size and the modification of probability mass function for the selection of the backoff value. The simulation results demonstrate that the proposed schemes can not only remarkably improve system throughput, but also provide a fair transmission environment.

Utilizing Energy Efficiency and QoS awareness for Receiver-Arbitrated and Sender-Predicted MAC Protocol in Wireless Sensor Network

In this paper, we exploit energy-efficiency and QoS-awareness for receiver-arbitrated and sender-predicted MAC Protocol (EQAP-MAC) in wireless sensor network. EQAP-MAC improves the transmission concurrency by separating control stream from data stream; minimizes energy consumption by sender by predicting receiver’s wake-up time; supports priority-based QoS through priority-based data flow selected, arbitrated and interrupted mechanisms; reduces transmission interference between the nodes by interleaving neighbor nodes’ wake-up time; improves system throughput and reduces the data stream transmitting delay by pipelining mechanism and optimizing send sequence. We have implemented EQAP-MAC on TinyOS and evaluated its performance on testbed. The result shows EQAP-MAC achieves higher concurrent performance and priority-based QoS guarantee with lower energy consumption compared with RI-MAC and X-AMC.

Ground target finding mechanism for unmanned aerial vehicles to secure crop field data

AbstractUnmanned aerial vehicles (UAVs) are being used in many military and civil applications while their use along wireless sensors is emerging abruptly as it upgrades the network in terms of reliability, lifetime, and connectivity. The system performance and throughput can be further enhanced if the UAVs in use are capable to locate ground sensor nodes as a means to address them smartly. In typically localization, the direction of arrival of incoming signals from a target node is measured by using a uniformed linear array (ULA) of antennas having signal processing capabilities. However, adding numerous antennas (transmitter receivers set) on a UAV is an extra burden which ultimately effects its speed, flight time, agility, and payload capacity. Considering the issue, a typical ULA antenna system is modeled by using virtual phased array (VPA) antenna. In the proposed VPA, a single antenna installed on a moving UAV will achieve the desire results. The well‐known multiple signal classification algorithm is modified to adapt this virtual antenna system. Four extra modules including calibrator, virtual antenna, rectifier, and adjustment are added in classical multiple signal classification algorithm to make it enable to work with single antenna. The proposed system mitigates the existing challenges of physical ULA and improves system throughput. Furthermore, due to its virtualness, VPA can adjust the number of antenna elements and interelement spacing, which adds multifrequency support and adaptive precision mechanism. A simulation model is developed in MATLAB to verify the performance of the VPA where the proposed system is evaluated against different scenarios with a varying number of parameters. It is found that VPA gives the same resolution and precision as a physical ULA antenna but it is more flexible, user‐friendly, cost‐effective, reliable, and most importantly, does not affect the UAV characteristics.

Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in Universities

How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure tasks, course design tasks submitted by undergraduate students, deep learning tasks and and so forth. Taking the actual needs of our laboratory as an instance, these tasks are analyzed, and scheduled respectively by different scheduling strategies. The Batch Scheduler is designed to process tasks in rush time to improve system throughput. Dynamic scheduling algorithm is proposed to tackle long-term lifecycle tasks such as deep learning tasks which are hungry for GPU resources and have dynamically changing priorities. Experiments show that the scheduling strategies proposed in this paper improve resource utilization and efficiency.

Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

As one of the key technologies in the fifth generation of mobile communications, massive multi‐input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single‐cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero‐forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single‐cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single‐cell perfect CSI scenario. In the single‐cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.

Resource Allocation and Optimization in Device-to-Device Communication 5G Networks

The next-generation wireless networks are expected to provide higher capacity, system throughput with improved energy efficiency. One of the key technologies, to meet the demand for high-rate transmission, is device-to-device (D2D) communication which allows users who are close to communicating directly instead of transiting through base stations, and D2D communication users to share the cellular user chain under the control of the cellular network. As a new generation of cellular network technology, D2D communication technology has the advantages of improving spectrum resource utilization and improving system throughput and has become one of the key technologies that have been widely concerned in the industry. However, due to the sharing of cellular network resources, D2D communication causes severe interference to existing cellular systems. One of the most important factors in D2D communication is the spectrum resources utilization and energy consumption which needs considerable attention from research scholars. To address these issues, this paper proposes an efficient algorithm based on the idea of particle swarm optimization. The main idea is to maximize the energy efficiency based on the overall link optimization of D2D user pairs by generating an allocation matrix of spectrum and power. The D2D users are enabled to reuse multiple cellular user’s resources by enhancing their total energy efficiency based on the quality of service constraints and the modification of location and speed in particle swarm. Such constraint also provides feasibility to solve the original fractional programming problem. Simulation results indicate that the proposed scheme effectively improved the energy efficiency and spectrum utilization as compared with other competing alternatives.

Research on Routing Strategy in Cluster Deduplication System

A cluster deduplication system can coordinate the work of multiple nodes, which can better alleviate the disk index bottleneck existing in the large-scale data backup system. However, there is a problem of isolated islands of information among nodes during data deduplication. When the servers use the query mode to route data, a large amount of system overhead is required to ensure a high deduplication rate and low throughput rate. At the same time, while the servers cannot obtain a higher deduplication rate if the servers adopt the stateless routing method. Data routing strategy can greatly affect the overall performance of the system. The concept of data frequency is proposed in this paper, and the classified routing strategy is designed. In the metadata server, a byte-shaped Bloom filter for recording the occurrence frequency of data blocks is maintained to record the occurrence frequency of data blocks. The values in the Bloom filter are counted. Then the frequency of the data blocks is compared with the configured threshold value to determine whether the data is “hot data”. We use stateful routing to send “clod data” to the storage nodes and use stateless routing to send the hot data to the storage nodes. Experimental results show that the classifying routing algorithm based on the frequency of data can greatly reduce the overhead of the system while guaranteeing the deduplication rate of the deduplication system as well as improve system throughput and real-time processing capabilities. Compared with the fully stateful routing scheme, our method only loses less than 2% of the deduplication rate, which reduces the communication query overhead by more than 25% and improves the real-time processing capability of the storage system.