Maximize System Throughput Research Articles

Allocation of flows in closed bipartite queueing networks

This paper describes a novel method for allocating agents to routes in a closed bipartite queueing network to maximize system throughput using three open network approximations. Results are presented which compare this method with known prior work and optimal solutions to provide an empirical optimality gap. Average empirical optimality gaps of 1.29 percent, 1.13 percent and 1.29 percent are observed for the three approximations considered. Further, because many systems are under the control of rational agents, conditions are derived in order to determine properties of the market context that induce optimal behavior. It is shown that uniform rewards do not yield an efficient rational equilibrium in general. However, for systems with homogeneous servers and travel times or those with travel times that are much larger than queue waiting times, uniform rewarding is optimal.

The design of data storage system based on Lustre for EAST

The quasi-steady state operation is one of the main purposes of EAST tokamak, and more than 400s discharge pulse has been achieved in the past campaigns. The acquired data amount increases continuously with the discharge length. At the same time to meet the requirement of the upgrade and improvement of the diagnostic systems, more and more data acquisition channels have come into service. Some new diagnostic systems require high sampling rate data acquisition more than 10MSPS. In the last campaign 2014, the data streaming is about 2000MB/s and the total data amount is more than 100TB. How to store the huge data continuously becomes a big problem. A new data storage system based on Lustre has been designed to solve the problem. All the storage nodes and servers are connected to InfiniBand FDR 56Gbps network. The maximum parallel throughput of the total storage system is about 10GB/s. It is easy to expand the storage system by adding I/O nodes when more capacity and performance are required in the future. The new data storage system will be applied in the next campaign of EAST. The system details are given in the paper.

Optimal Power Allocation for Maximum Throughput of General MU-MIMO Multiple Access Channels With Mixed Constraints

Based on the efficient generalized water-filling with group peak power constraints (GWFGP), this paper proposes an iterative algorithm to compute the optimal solutions to system throughput (sum-rate) maximization problems. This class of problems is equipped with the multiuser multiple input multiple output multiple access channels (MU-MIMO MAC) in the general communication systems. The proposed iterative GWFGP algorithm (IGWFGP) has two levels of loops. The inner loop aims at computing the solution to each member in the family, while the outer loop aims at computing the solution to the target problem based on the results obtained by the inner loop. Both GWFGP and the convergence theory of an algorithm are used in the inner loop and the outer loop respectively. Furthermore, by exploiting the concept of variable weighting factor for covariance update, IGWFGP owns fast convergence and provides optimal solutions to the sum rate maximization problems. The usage of the convergence theory in IGWFGP and the algorithm of GWFGP are efficient and novel. To the best of the authors’ knowledge, no prior algorithm has been reported in the open literature to solve the targeted problem in this paper. In addition, the proposed algorithm does not require to choose the initial value for computation. This feature is a significant advantage of the algorithm, especially for large and complicated systems.

Analysis and optimization of delayed channel access for wireless cyber-physical systems

In wireless cyber-physical systems (CPS), with a growing number of sensors being deployed, more and more data will be transmitted over wireless links. This essentially requires that wireless network protocols should be further studied and amended to improve their transmission efficiency. The delayed channel access (DCA) protocol is of great practical importance in improving channel utilization. DCA is an improved version of IEEE 802.11 distributed coordination function (DCF). In DCA, a node first waits for an extra delay before it enters the normal DCF procedure, so that more packets can be aggregated and transmitted upon each transmission opportunity. However, how the extra delay affects the performance of DCA has never been theoretically investigated. In this paper, we first propose a theoretical model to characterize the impact of a deterministic extra delay on collision probability, throughput, and medium access control (MAC) delay. With this model, we perform asymptotic analysis to calculate the optimal deterministic delay that can maximize the system throughput. We find that the system performance is significantly affected by the relationship between the deterministic delay and the number of nodes; and therefore, carefully choosing a suitable deterministic delay is crucial in improving channel utilization. The extensive ns2 simulations verify that our model is very accurate, and the theoretical optimal deterministic delay can make the system achieve the maximum system throughput. This study is very useful for better designing and implementing the packet aggregation technology that has been adopted by IEEE wireless networking standards including 802.11n and the latest 802.11ac.

Differentiating and Scheduling LTE Uplink Traffic Based on Exponentially Weighted Moving Average of Data Rate

The importance of uplink resource allocation in ensuring the Quality of Service (QoS) of guaranteed bit rate (GBR) bearers has led to the development of numerous resource allocation schemes. The criteria used in the development of such schemes include maximizing system throughput or fairness and the ability to take transmission power or user priority into account. After accepting a GBR bearer, the eNodeB (eNB) must allocate sufficient resource blocks (RBs) to guarantee the requested QoS. In order to achieve high utilization of radio resource, eNB also must allocate remaining RBs to non-GBR bearers. The QoS is generally specified by the data rate (throughput) or packet delay; however, the 3rd Generation Partnership Project (3GPP) has not specified the means by which data rates for a GBR bearer are to be measured. In this paper, we define the measurement of data rates based on the exponentially weighted moving average (EWMA), thereby enabling the eNB to perform scheduling tasks with greater accuracy. We also present an AAG-2 (Allocate As Granted-version 2) scheme capable of supporting the QoS for GBR bearers, while enabling the efficient allocation of RBs to non-GBR bearers. The existence of parties seeking superior levels of service makes it necessary for telecommunications operators to provide options with regard to QoS. This study proposes a revision of the AAG-2 scheme referred to as AAG-D to accommodate such demands. Simulation results demonstrate the efficacy of the new scheme in achieving goals related to throughput and average packet delay.

Interference-Aware Resource-Sharing Scheme for Multiple D2D Group Communications Underlaying Cellular Networks

Device-to-device (D2D) communications underlaying cellular networks have the potential to improve spectrum efficiency and link capacity by allowing nearby devices to communicate directly with each other on the licensed frequency bands. However, co-channel interference between cellular users (CUEs) and D2D pairs and co-channel interference among D2D pairs are major issues to be solved. In this paper, we propose an efficient interference-aware frequency resource-sharing scheme for multiple D2D groups that can efficiently maximize system throughput by considering grouping method, adaptive antenna arrays, and application of interference alignment (IA) for the D2D communications. Using a grouping method, nearby D2D pairs can form D2D groups for the convenience of implementing IA to cancel the interference among the D2D pairs in the group. Interference from the eNB to D2D pairs is reduced by the use of beamforming at the eNB. Furthermore, a greater distance between the D2D pairs and CUEs assists in reducing the interference between them. System-level simulation results confirm that the proposed scheme improves cell throughput compared with conventional distance sharing and random sharing schemes by 8.3 and 23.8 %, respectively. The proposed scheme also demonstrates high cell throughput gain in comparison to the scenario of "without IA".

Joint user grouping and resource allocation for uplink virtual MIMO systems

MIMO has become a core technology of 5G network to largely improve system throughput. Due to the cost and size of the user equipment (UE), the application of MIMO uplink is limited by the difficulty in practical implementation at the user side. Virtual MIMO has been widely investigated to solve this problem for wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference. To obtain good trade-off between the system throughput and transmission performance, we investigate joint user grouping and resource allocation under the consideration of system throughput and average mean squared error (MSE) performance in SC-FDMA uplink systems. Based on linear MIMO detection, we first develop MSE-oriented user grouping criteria for evaluation of transmission performance, then establish dynamic user grouping and optimal resource allocation problems for hard and elastic average MSE constraints. The proposed joint resource allocation algorithm is evaluated in SC-FDMA uplink scenarios and the results show that it achieves maximum system throughput with average MSE guaranteed for the hard MSE constraint algorithms and the alterable trade-off between system throughput and average MSE for the elastic MSE constraint algorithms.

Multi-Armed Bandit Channel Access Scheme With Cognitive Radio Technology in Wireless Sensor Networks for the Internet of Things

The wireless sensor network (WSN) is one of the key enablers for the Internet of Things (IoT), where WSNs will play an important role in future internet by several application scenarios, such as healthcare, agriculture, environment monitoring, and smart metering. However, today's radio spectrum is very crowded for the rapid increasing popularities of various wireless applications. Hence, WSN utilizing the advantages of cognitive radio technology, namely, cognitive radio-based WSN (CR-WSN), is a promising solution for spectrum scarcity problem of IoT applications. A major challenge in CR-WSN is utilizing spectrum more efficiently. Therefore, a novel channel access scheme is proposed for the problem that how to access the multiple channels with the unknown environment information for cognitive users, so as to maximize system throughput. The problem is modeled as I.I.D. multi-armed bandit model with M cognitive users and N arms (M<;N). In order to solve the competition and the fairness between cognitive users of WSNs, a fair channel-grouping scheme is proposed. The proposed scheme divides these channels into M groups according to the water-filling principle based on the learning algorithm UCB-K index, the number of channels not less than one in each group and then allocate channel group for each cognitive user by using distributed learning algorithm fairly. Finally, the experimental results demonstrate that the proposed scheme cannot only effectively solve the problem of collision between the cognitive users, improve the utilization rate of the idle spectrum, and at the same time reflect the fairness of selecting channels between cognitive users.

Packet Scheduling Study For Heterogeneous Traffic In Downlink 3gpplte System

Long Term Evolution (LTE) network deploys Orthogonal Frequency Division Multiple Access (OFDMA) technology for downlink multi-carrier transmission. To meet the Quality of Service (QoS) requirements for LTE networks, packet scheduling has been employed. Packet scheduling determines when and how the user’s packets are transmitted to the receiver. Therefore effective design of packet scheduling algorithm is an important discussion. The aims of packet scheduling are maximizing system throughput, guaranteeing fairness among users, andminimizing either or both PacketLoss Ratio (PLR)and packet delay. Inthis paper, the performance of two packet scheduling algorithms namely Log Maximum-Largest Weighted Delay First (LOG-MLWDF) and Max Delay Unit (MDU), developed for OFDM(Orthogonal Frequency Division Multiplexing)networks, has been investigated in LTE downlink networks, and acomparison of those algorithmswith a well-known scheduling algorithm namely Maximum-Largest Weighted Delay First(MLWDF) has been studied.The performance evaluation was in terms of system throughput, PLR and fairness index. This study was performed forboth real time (voice and video streaming)and non-real time (best effort)perspectives. Results show that for streaming flows,LOG-MLWDF shows best PLR performance among the considered scheduling schemes, and for best effort flows, it outperforms theother two algorithms in terms of packet delay and throughput.

Throughput-Optimal Scheduling Design With Regular Service Guarantees in Wireless Networks

Motivated by the regular service requirements of video applications for improving Quality-of-Experience (QoE) of users, we consider the design of scheduling strategies in multi-hop wireless networks that not only maximize system throughput but also provide regular inter-service times for all links. Since the service regularity of links is related to the higher-order statistics of the arrival process and the policy operation, it is highly challenging to characterize and analyze directly. We overcome this obstacle by introducing a new quantity, namely the time-since-last-service (TSLS), which tracks the time since the last service. By combining it with the queue-length in the weight, we propose a novel maximum-weight type scheduling policy, called Regular Service Guarantee (RSG) Algorithm. The unique evolution of the TSLS counter poses significant challenges for the analysis of the RSG Algorithm. To tackle these challenges, we first propose a novel Lyapunov function to show the throughput optimality of the RSG Algorithm. Then, we prove that the RSG Algorithm can provide service regularity guarantees by using the Lyapunov-drift based analysis of the steady-state behavior of the stochastic processes. In particular, our algorithm can achieve a degree of service regularity within a factor of a fundamental lower bound we derive. This factor is a function of the system statistics and design parameters and can be as low as two in some special networks. Our results, both analytical and numerical, exhibit significant service regularity improvements over the traditional throughput-optimal policies, which reveals the importance of incorporating the metric of time-since-last-service into the scheduling policy for providing regulated service.

Energy-aware two-way relaying networks under imperfect hardware: optimal throughput design and analysis

In this paper, we propose a new formula for achieving optimal throughput in energy-aware cooperative networks with generic time and power energy harvesting protocol, namely time power switching based relaying (TPSR). Especially, this investigation analyzes the impact of imperfect hardware at the relay node and the destination node in the two-way relaying networks (TWRN). This analysis enables us to derive the closed-form expressions of outage probabilities of signal-to-noise and distortion ratio (SNDR) at the destination nodes under the effect of hardware impairments. Interestingly, the optimal policy of joint wireless information and energy transfer is designed to maximize the system throughput by finding the optimal time switching and power splitting fractions in the proposed TPSR protocol. An important achievement is that the proposed optimal design offers the maximum throughput of system when we consider the trade-off between throughput and time-power factors in energy harvesting protocol by both numerical method and simulation. Numerical results provide practical insights into the performance of energy-aware TWRN under hardware impairments. Monte-Carlo method is also deployed to corroborate the accuracy of analytical derived expressions.

Robust Rate Adaptation and Proportional Fair Scheduling With Imperfect CSI

In wireless fading channels, multi-user scheduling has the potential to boost the spectral efficiency by exploiting diversity gains. In this regard, proportional fair (PF) scheduling provides a solution for increasing the users' quality of experience by finding a balance between system throughput maximization and user fairness. For this purpose, precise instantaneous channel state information (CSI) needs to be available at the transmitter side to perform rate adaptation and scheduling. However, in practical setups, CSI is impaired by, e.g., channel estimation errors, quantization and feedback delays. Especially in centralized cloud based communication systems, where main parts of the lower layer processing is shifted to a central entity, high backhaul latency can cause substantial CSI imperfections, resulting in significant performance degradations. In this work, robust rate adaptation as well as robust PF scheduling are presented, which account for CSI impairments. The proposed rate adaptation solution guarantees a fixed target outage probability, which is of interest for delay critical and data intensive applications, such as, video conference systems. In addition to CSI imperfections, the proposed scheduler is able to account for delayed decoding acknowledgements from the receiver.

Energy Aware Optimal Resource Allocation in Backhaul Constraint Wireless Networks: A Two Base Stations Scenario

In future wireless communication systems, the capacity constrained backhaul gradually becomes bottleneck both in spectrum efficiency and energy efficiency, especially in joint processing of LTE-Advanced. This paper addresses the issue of energy aware resource allocation with limited backhaul capacity in uplink cooperative reception, where two base stations (BSs) equipped with single-antenna each serving multiple users with single-antenna via multicarrier are considered. We propose a novel energy efficient cooperative scheme based on compress-and-forward and user pairing to solve the problem in two base stations scenario. In order to maximize system throughput and increase energy efficiency under the limited backhaul capacity constraint, we formulate the joint optimization problem of user pairing, subcarrier mapping, and backhaul capacity sharing between different pairs (subcarriers). An energy efficient algorithm based on alternating optimization strategy and perfect mapping is proposed to solve this mixed integer programming problem. Simulations show that this allocation algorithm can improve the system capacity and energy efficiency significantly compared with the blind alternatives.

Wireless Information and Power Transfer: Optimal Power Control in One-Way and Two-Way Relay System

This paper considers optimal power control solutions in one-way and two-way relay system with simultaneous wireless information and power transfer (SWIPT). First, we develop SWIPT protocols for one-way and two-way relay systems, where two terminals exchange data assisted by an energy-harvesting relay node which gathers energy from the received signal by using a power splitting scheme and forwards the received signal by using the harvesting energy. For one-way relay system, the closed-form solution for optimal power splitting ratio is derived to maximize the system throughput. For asymmetric two-way relay system, we propose a novel power splitting strategy to maximize system throughput. Considering the total transmit power constraints, we develop a semi-closed form solution for power allocation among all nodes by exploiting convex optimization technology. It is shown that the optimal power allocation in two-way relay system would degrade into one-way relay mode if the relay node is close to one of the terminals. For symmetric two-way relay system, a closed-form solution for power allocation and power split ratio is derived. The performances of the three systems are compared numerically. It is revealed that the proposed power allocation algorithm can improve the transmission rate without consuming additional resource compared with non-cooperation relaying scheme.

Game Theoretic Max-logit Learning Approaches for Joint Base Station Selection and Resource Allocation in Heterogeneous Networks

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.

Energy-Efficient Power Allocation for Cognitive Radio Networks with Joint Overlay and Underlay Spectrum Access Mechanism

Traditional designs of cognitive radio (CR) focus on maximizing system throughput. In this paper, we study the joint overlay and underlay power allocation problem for orthogonal frequency-division multiple access–based CR. Instead of maximizing system throughput, we aim to maximize system energy efficiency (EE), measured by a “bit per Joule” metric, while maintaining the minimal rate requirement of a given CR system, under the total power constraint of a secondary user and interference constraints of primary users. The formulated energy-efficient power allocation (EEPA) problem is nonconvex; to make it solvable, we first transform the original problem into a convex optimization problem via fractional programming, and then the Lagrange dual decomposition method is used to solve the equivalent convex optimization problem. Finally, an optimal EEPA allocation scheme is proposed. Numerical results show that the proposed method can achieve better EE performance.

Providing Vehicular Infotainment Service Using VHF/UHF TV Bands via Spatial Spectrum Reuse

In this paper, we identify the potential of the television bands to be used for providing vehicular infotainment service. Specifically, content delivery from the roadside unit to vehicular users is conducted by exploiting the spectrum holes, which is also referred to as spatial spectrum reuse. In this manner, the spectrum utilization efficiency is improved and the digital television service is not interfered. We first give a thorough description on such vehicular content delivery network and indicate the major design issues. After that, we respectively develop user and resource models tailored for our scenario by considering their characteristics and formulate the resource allocation as a system throughput maximization problem based on these models. For practical implementation, we devise a vehicular content delivery protocol incorporating a dynamic spectrum access algorithm and a maximal frame size design method, both of which aim at throughput and spectrum efficiency enhancement. Extensive simulations are conducted and the numerical results show the system performance is improved with our approaches.

Fairness aware rate adaptation and proportional scheduling for IEE 802.11 wlans using FSE

With its rapid development in the wireless markets, IEEE 802.11 WLAN is experiencing a huge popularity. However, due to the limitation of frequency bandwidth of WLANs, it is essential that the available radio resource should be fully utilized to offer different services to multiple users. In order to maximize system throughput while still guaranteeing the fairness among users, a proportional fairness based algorithm is proposed in this work. Since most of the previous resource allocation algorithms were simply based on the channel conditions without taking into account user’s demand, in this paper, we introduce the theory of fuzzy synthetic evaluation (FSE) which also allows us to consider user’s demand as an important factor. As such, the fairness among users can be improved based on different users’ requirements for services. In addition, a channel state information based rate adaptation scheme is also proposed. Through simulation studies, the results clearly validate that our proposed scheme shows advantages on providing user fairness while still improving the system throughput.

Access point selection game with mobile users using correlated equilibrium.

One of the most important issues in wireless local area network (WLAN) systems with multiple access points (APs) is the AP selection problem. Game theory is a mathematical tool used to analyze the interactions in multiplayer systems and has been applied to various problems in wireless networks. Correlated equilibrium (CE) is one of the powerful game theory solution concepts, which is more general than the Nash equilibrium for analyzing the interactions in multiplayer mixed strategy games. A game-theoretic formulation of the AP selection problem with mobile users is presented using a novel scheme based on a regret-based learning procedure. Through convergence analysis, we show that the joint actions based on the proposed algorithm achieve CE. Simulation results illustrate that the proposed algorithm is effective in a realistic WLAN environment with user mobility and achieves maximum system throughput based on the game-theoretic formulation.

Deployment analysis and optimization of heterogeneous networks under the spectrum underlay strategy

To meet the growing mobile traffic demand, deployment of heterogeneous networks (HetNets) is proposed as a solution to enhance network capacity. It will enable the coexistence of several systems consisting of Macro, Micro, and Femto or WiFi layers. However, the main issue is the coexistence due to mutual interference. To guarantee that HetNet performance will not be compromised due to the interference, some deployment parameters should be controlled, such as primary exclusion zones and density of users. In this context, the present work provides deployment analysis and optimization for a Macro-Femto scenario, considering interference constraints to maximize the data rate of the HetNet. To fulfill this, we characterize some spectrum underlay scenarios in which HetNets would be expected to operate, setting different signal-to-interference-plus-noise ratio (SINR) thresholds. They are evaluated using a solution procedure based on the socio-cognitive particle swarm optimization (SCPSO) algorithm. Considering the best scenarios found, we show deployment parameters to achieve maximum system throughput, such as density of femto-users and primary exclusion zones of macro-users.