Improve System Throughput Research Articles

Joint AP grouping and user clustering for interference management in Cell-Free VLC network

As a candidate complementary to radio frequency (RF) communications, visible light communication (VLC) is a promising technology with the advantages of high date rate, low power consumption and high security. Since the small light emitting diode (LED) coverage may cause the frequent handover of users, cell-free network model with low handover overhead is utilized in this paper. Meanwhile, the massive and random deployment of users in cell-free VLC network may cause severe interference, which deteriorates the system performance. Then, for reducing interference, we put forward a joint AP Grouping and User Clustering for Interference Management (AGUC-IM) in cell-free VLC network. Firstly, the weighted undirected interference graph of AP is constructed according to user-AP access, and an AP grouping algorithm for group number optimization is designed for mitigating interference and improving resource utilization. Thereafter, a time–frequency resource optimization based on twice user clustering algorithm is designed to mitigate the residual interference of users and improve user satisfaction. Simulation results show that, compared with the existing algorithms, the proposed algorithm effectively improves the user satisfaction while significantly improving system throughput.

Dynamic thread mapping for power-efficient many-core systems under performance constraints

Efficiently mapping threads to system cores is critical for achieving high performance and power efficiency in multicore systems. Conventional scheduling techniques do not take system heterogeneity and varying voltage/frequency states into account and therefore do not produce efficient mappings. Additionally, since thread behavior varies throughout its execution, a fixed thread-to-core mapping is sub-optimal. Dynamically mapping threads to appropriate core states can significantly improve system throughput and decrease energy consumption. Mapping decisions are of utmost importance in systems that have performance constraints to satisfy. The problem of thread assignment in order to meet performance or power constraints has been shown to be NP-complete and therefore exceedingly expensive to solve online. This paper proposes a heuristic method for dynamically assigning threads to system cores such that performance requirements are met while minimizing the energy consumed. This method can be applied to systems consisting of heterogeneous cores, and systems with dynamic voltage/frequency scaling capabilities. Simulation results show that the proposed heuristic can be used online in hundred-core systems, with runtime overhead of less than 1 millisecond for 256-core systems.

Low complexity closed‐loop strategy for mmWave communication in industrial intelligent systems

Modern communication and computing technology is the basic support of the industrial intelligent systems (IIS). As a key component of IIS, the smart port is essential to be offered low-complexity and high-reliability communication service, especially for driverless engineering vehicles. However, it is combined and nonconvex to find the optimal association between vehicles and the road side units (RSUs). Besides, due to the mobility of vehicles and the severe path loss of mmWave links, beam switching and reassociation between vehicles and RSUs are required frequently, which brings a great challenge to the communication for the IIS. A low complexity closed-loop strategy based on distributed cooperation for mmWave communication in IIS is proposed in this study, in which user association and beam tracking with the assistance of beam pools is proposed. Many-to-many user association is established based on distributed multiagent reinforcement learning, where the vehicle can independently select the set of serving RSUs based on the local observation without information exchange with others, reducing the signaling overhead and computational complexity while improving system throughput. Furthermore, multipoint-cooperation soft switching of beams based on beam tracking improves the reliability of mmWave communication with the smaller training cost. Extensive analysis and simulation results demonstrate that the proposed solution significantly reduces the complexity of the mmWave communication while improving the throughput and stability in IIS.

Relay Selection-Based Cooperative Backscatter Transmission With Energy Harvesting: Throughput Maximization

In this letter, an optimal time allocation scheme is proposed to improve the throughput of energy harvesting based backscatter communication. The base station (BS) serving as the power supply is transmitting continuously with constant power. All the relays can harvest radio frequency (RF) energy from the transmission of BS while the user performs backscatter transmission, and then the selected relay decodes and forwards the information from user with harvested RF energy. We propose an optimal time allocation scheme that considers the adaptive backscatter coefficient and relay selection to maximize throughput. Simulation results demonstrate that the proposed scheme outperforms the benchmark strategies in system throughput improvement.

Orthogonal frequency division multiplexing system with an indexed-pilot channel estimation

In this paper, we examine an orthogonal frequency division multiplexing (OFDM) system under imperfect channel conditions and pilot-insertion- based channel estimation. However, unlike conventional pilot-insertion-based channel estimation, some inserted pilot symbols are set to zero where the index of the zero-pilot symbols is employed to transmit extra data bits. In this paper, we employ a minimum mean squared error (MMSE) to detect transmitted pilot symbols; these symbols are then used to estimate channel coefficients. Furthermore, the impact of zero-pilot symbols on the mean-squared error of channel estimation and on system error performance is examined. Our findings show that the index of zero-pilot symbols can be used to improve system throughput by carrying extra information bits without harming channel estimation accuracy or degrading system error performance. Simulation results show that, at a high signal-to-noise ratio, the bit error rate for data bits transmitted via zero-pilot symbols index selection is lower than that of data bits transmitted over data subcarriers.

Portrait: A holistic computation and bandwidth balanced performance evaluation model for heterogeneous systems

Accelerators are widely used in specific domains ranging across deep learning, streaming computation and database query. To enable an accelerator, it has to be attached to a primary controller, typically, a CPU. This kind of CPU-accelerator heterogeneous systems is the mainstream of current computer systems. In CPU-accelerator heterogeneous systems, customized optimization for accelerators boosts performance and energy efficiency. However the bandwidth contention of CPU-accelerator interconnection and hardware hazard between multiple tasks on accelerators significantly bottleneck the designed performance. On one hand, limited interconnected bandwidth resource causes bandwidth contention in task offloading from CPU to accelerators. On the other hand, limited hardware resources on accelerators cause hardware hazard during task execution. To take fully advantage of designed computing power in CPU-accelerator heterogeneous system, it is necessary to mitigate these kinds of contention. However, it is hard for programmers and users to solve the contention because of the complexity of both computing tasks and system behaviors. In state-of-the-art, CPU–GPU heterogeneous system have been sufficiently studied. But CPU–FPGA heterogeneous systems are seldom comprehensive analyzed. To help to address this problem, we propose a holistic profiling system, Portrait, to help to model both computation and bandwidth resource in CPU-accelerator heterogeneous system and quantify bandwidth requirement and execution time of given tasks. The experiment shows that Portrait increases the accuracy of the bandwidth requirement up to 97.71% on average, which is 1.95x compared with the state-of-the-art. It also provides a more accurate of computation latency compared to the state-of-the-art that failed to evaluate accelerator behaviors. And it increases accuracy of task execution latency over 97.47% on average. Additionally, based on precise profiling of CPU-accelerator heterogeneous system, Portrait could help task scheduling to mitigate bandwidth contention and hardware hazard more effectively to improve system throughput.

Resource allocation based on multi-grouping and frame expansion for NOMA backscatter communication network

Non-orthogonal multiple access (NOMA) and Backscatter Communication (BackCom) have great development potential in the Internet of Things (IoT). However, the power regulation and energy harvesting capacity of backscatter node (BN) in backscatter network are limited, and it is necessary to explore an appropriate resource allocation scheme to combine NOMA with BackCom. This work presents a resource allocation scheme based on multi-grouping and frame expansion to ensure reliable communication. The BNs are grouped twice to improve the total power by setting more accurate and appropriate reflection coefficients for BNs. Considering the difference of energy harvesting efficiency between BNs, different time slot lengths are set to ensure the energy supply of BNs, and the selection criterion of minimum time slot length is given. The experimental results show that the proposed scheme can effectively improve system throughput and decoding performance.

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

An efficient resource optimization scheme for D2D communication

With the rapid development of wireless technologies, wireless access networks have entered their Fifth-Generation (5G) system phase. The heterogeneous and complex nature of a 5G system, with its numerous technological scenarios, poses significant challenges to wireless resource management, making radio resource optimization an important aspect of Device-to-Device (D2D) communication in such systems. Cellular D2D communication can improve spectrum efficiency, increase system capacity, and reduce base station communication burdens by sharing authorized cell resources; however, can also cause serious interference. Therefore, research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance. This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network. The proposed algorithm uses the fuzzy clustering method, which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users. An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency. Simulation results show that these proposed algorithms can effectively improve system throughput, reduce co-channel interference, and enhance energy efficiency.

Hierarchical Symbiotic Transmission Strategy With Cooperative-NOMA for Cognitive Radio Networks

We propose a hierarchical symbiotic transmission strategy with cooperative-nonorthogonal multiple access (C-NOMA) for multi-carrier cognitive radio networks, where the mutualistic and commensualistic transmission modes are employed in the primary network (PN) and secondary network (SN), by promoting some secondary users (SUs) as relays to assist communication of both PN and SN. The proposed strategy enables higher system throughput and supports much more user connectivity than the existing schemes with C-NOMA that focused on maintaining the performance of PN. Sub-carrier assignment and power allocation solutions are presented to further improve system throughput. Simulation results demonstrate the effectiveness of the proposed strategy.

Content Distribution Based on Joint V2I and V2V Scheduling in mmWave Vehicular Networks

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles.

RIS-Aided Hybrid Massive MIMO Systems Relying on Adaptive-Resolution ADCs: Robust Beamforming Design and Resource Allocation

The large-scale multiple-input multiple-output (MIMO) uplink is investigated in the presence of channel-induced uncertainty, where variable-resolution analog-to-digital converters (ADCs) are used at the base station (BS) and a reconfigurable intelligent surface (RIS) is employed for supporting communications between the single-antenna users and the multi-antenna BS. We formally maximize the system throughput by jointly optimizing the ADC’s resolution, the transmit power, the passive reflection coefficients of the RIS and the hybrid combiner of the BS subject to practical constraints under statistical cascaded channel state information (CSI) error model. The robust nonconvex optimization problem is firstly decoupled via the classic Lagrangian dual transform and fractional programming method, followed by a powerful decoupling-based alternating maximization (D-AltMax) algorithm to solve this challenging problem. Our simulation results reveal the supremacy of our proposed algorithm over the benchmark schemes by quantifying the improved system throughput of this robust design.

An efficient multi-level cache system for geometrically interconnected many-core chip multiprocessor

<p><span lang="EN-US">Many-core chip multiprocessor offers high parallel processing power for big data analytics; however, they require efficient multi-level cache and interconnection to achieve high system throughput. Using on-chip first level L1 and second level L2 per core fast private caches is expensive for large number of cores. In this paper, for moderate number of cores from 16 to 64, we present a cost and performance efficient multi-level cache system with per core L1 and last level shared bus cache on each bus line of a cost-efficient geometrically bus-based interconnection. In our approach, we extracted cache hit and miss concurrencies and applied concurrent average memory access time to more accurately determine the cache system performance. We conducted least recently used cache policy-based simulation for cache system with L1, with L1/L2, and with L1/shared bus cache. Our simulation results show that an average system throughput improvement of 2.5x can be achieved by using system with L1/shared bus cache system compared to using only first level L1 or L1/L2. Further, we show that the throughput degradation for the proposed cache system is only within 5% for a single bus fault, suggesting a good bus fault tolerance.</span></p>

An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput.

In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cell power control algorithm (SPC) and interference-managed hybrid clustering (IMHC) scheme, to resolve the issue of co-tier and cross-tier interference in the small cell base station cluster tiers. The small cell base stations (SBSs) are categorized based on their respective transmitting power, as high-power SBSs (HSBSs) and low-power SBSs (LSBSs). The simulation results show that by implementing the IMHC algorithm for SBSs in a three-tier heterogeneous network, the system throughput is improved with reduced interference.

Nebula: A Scalable and Flexible Accelerator for DNN Multi-Branch Blocks on Embedded Systems

Deep neural networks (DNNs) are widely used in many artificial intelligence applications; many specialized DNN-inference accelerators have been proposed. However, existing DNN accelerators rely heavily on certain types of DNN operations (such as Conv, FC, and ReLU, etc.), which are either less used or likely to become out of date in future, posing challenges of flexibility and compatibility to existing work. This paper designs a flexible DNN accelerator from a more generic perspective rather than speeding up certain types of DNN operations. Our proposed Nebula exploits the width property of DNNs and gains a significant improvement in system throughput and energy efficiency over multi-branch architectures. Nebula is a first-of-its-kind framework for multi-branch DNNs.

Orthognal Zones for Interference Migration in 2.4 GHz Mesh Backhaul

Managing interference in the multi-radio networks is critical challenge; problem becomes even more serious in 2.4 GHz band due to minimal availability of orthogonal channels. This work attempts to propose a channel assignment scheme for interference zones of 2.4 GHz backhaul of Wireless Mesh Networks (WMN). The static nodes of Infrastructure based Backhaul employing directional antennas to connect static nodes, orthogonal channel zones introducing Interference are formatted with the selection of single tire direct hop and two tier directional hopes. The effort maintain the orthogonality of channels on system thus reduce the co-channel interference between inter flow and intra flow links. Group of non-overlapping channels of selected band are obtained by a mathematical procedure, interference is modeled by directed graph and Channel assignment is carried out with the help of greedy algorithms. Experimental analysis of the technical proposal is done by simulation through OPNET 14. Our framework can act as an imperative way to enhance the network performance resulting a leading improvement in system throughput and reduction in system delay

More Than Scheduling: Novel and Efficient Coordination Algorithms for Multiple readers in RFID Systems

How to efficiently coordinate multiple readers to work together is critical for high throughput in RFID systems. Existing researchs focus on designing efficient reader scheduling strategies that arrange adjacent readers to work in different time to avoid signal collisions. However, the impact of unbalanced tag number of readers on tag read throughput is still challenging. In RFID systems, the distribution of tags is usually variable and uneven, making the number of tags covered by each reader (i.e., the load) imbalanced. This imbalance leads to different execution time for readers: the heavily loaded readers take longer time to collect all tags, while the other readers whose finish execution earlier have to wait in vain. To avoid this useless waiting and improve the system throughput, this paper focuses on the load balancing problem of multiple readers, which is an NP-hard problem. In this paper, we design heuristic algorithms to adjust readers interrogation regions and efficiently balance their loads. The amazing advantage of our algorithm is that it can be adopted by almost all existing protocols in multi-reader systems, including the reader scheduling protocol, to improve system throughput. Extensive experiments demonstrate that our algorithm can significantly improve the throughput in various scenarios.

Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

With the rapid development of the next-generation mobile network, the number of terminal devices and applications is growing explosively. Therefore, how to obtain a higher data rate, wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of scholars. Device-to-Device (D2D) communication technology and other frontier communication technologies have emerged. Device-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular networks. It has become one of the key technologies of the fifth-generation mobile communications system(5G). D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization, enhance network coverage, reduce transmission delay and improve system throughput, but it would also bring complicated and various interferences due to reusing cellular resources at the same time. So resource management is one of the most challenging and importing issues to give full play to the advantages of D2D communication. Optimal resource allocation is an important factor that needs to be addressed in D2D communication. Therefore, this paper proposes an optimization method based on the game-matching concept. The main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching theory. Simulation results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

The study of dynamic resource allocation on aggregation of unlicensed spectrum in LTE-A networks

The 3GPP formulated the fourth-generation LTE-Advanced specifications, in which Carrier Aggregation technology can increase the data transmission rate by aggregating continuous and non-continuous carriers to meet the transmission needs of a large number of users. However, users' demand for multimedia network application services continues to increase and makes the licensed spectrum more and more overwhelming. Therefore, this article hopes to combine the licensed and unlicensed spectrum to provide a wider data channel with higher data transmission. We focus on improving the throughput of the downlink system, and propose a genetic algorithm to optimise the weights which referring to the carrier unit channel quality and load conditions to select the most suitable carrier unit for the user. Afterwards, resource allocation methods are presented for the different traffic of GBR and Non-GBR effectively. Finally, we show the simulation results to prove that the proposed method is effective in improving system throughput and user satisfaction.

Research on Data Routing Strategy of Deduplication in Cloud Environment

The application of data deduplication technology reduces the demand for data storage and improves resource utilization. Compared with limited storage capacity and computing capacity of a single node, cluster data deduplication technology has great advantages. However, the cluster data duplication technology also brings new issues on deduplication rate reduction and load balancing of storage nodes. The application of data routing strategy can well balance the problem of deduplication rate and load balancing. Therefore, this paper proposes a data routing strategy based on distributed Bloom Filter. 1)Superchunk is used as the basic unit of data routing to improve system throughput. According to Broder’s theorem, k leastsized fingerprints are selected as the Superchunk features and send to the storage node. The optimal node is selected as the routing node by matching the BloomFilter, and the storage capacity of the node and maintained in the memory of the storage node. 2) Design and implement system prototypes. The specific parameters of all kinds of routing strategies are obtained through experiments, and the routing strategies proposed in this paper are tested. The theoretical analysis and experimental results prove the feasibility of the strategies proposed by this paper. Compared with the other routing strategies, our method improved 3% of the deduplication rate, reduces the communication query overhead by more than 36% and improves the load balancing degree of the storage system.