Short-term Throughput Research Articles

DeX: Deep learning-based throughput prediction for real-time communications with emphasis on traffic eXtremes

Recent years have witnessed a remarkable upsurge in the global proliferation of Real-Time Communications (RTC) applications, a trend propelled by the flourishing advancement of network technologies and further amplified by the COVID-19 pandemic. Within this context, there is a burgeoning interest in the innovation of sophisticated and intelligent network infrastructures and technologies. Positioned as a promising candidate for this purpose, real-time throughput prediction emerges as a key enabler to foster network observability and offer proactive functions, upholding advanced system management, including but not limited to, bandwidth allocation and adaptive streaming. Nonetheless, existing methodologies struggle with predicting extreme conditions of throughput, notably peaks, valleys, and abrupt changes, that are critical in RTC traffic. To surmount these obstacles, we introduce DeX, a Deep Learning (DL)-based framework, designed to predict short-term throughput, with a dexterous proficiency and dedicated focus on navigating the complexities of traffic eXtremes. In particular, DeX leverages solely packet-level information as features and is composed of three integral components: a packet selection module that opts for an optimal subset of input features, a feature extraction block that partially incorporates the Transformer architecture, and a multi-task learning pipeline that improves the proficiency in handling traffic extremes. Moreover, our work is anchored in extensive traffic traces garnered during actual video-teleconferencing calls, and we formulate a time-series regression problem, rigorously evaluating a spectrum of technologies ranging from an adaptive filter to diverse Machine Learning (ML) and DL approaches. Initially, we aim at predicting throughput within 500-ms time windows using historical 1024 packets out of 2048, and consequently, our methodology exhibits exceptional efficacy, especially in forecasting traffic extremities. Conclusively, we conduct a series of ablation experiments and thorough analyses to showcase the enhanced performance of various scenarios, further validating the effectiveness and robustness of DeX.

Investigation on the Long-Term Stability of Multiple Salt Caverns Underground Gas Storage With Interlayers

Abstract Salt cavern underground gas storage (UGS) has attracted more and more attention worldwide for high peak shaving efficiency and high short-term throughput. To ensure the safe operation of this type of UGS, it is necessary to evaluate and analyze its stability. This paper investigates the influences of interlayers and cavern interactions on salt cavern UGS's stability. A 3D geomechanical model of double-salt cavern UGS with interlayers is established based on the geological data and creep constitutive relation of salt rock. Based on the long-term creep numerical simulation, the influences of interlayer number, interlayer thickness, interlayer dip angle, interlayer stiffness, cavern spacing, and cavern pressure difference on the deformation of caverns and stability performance of UGS are studied. The results show that the UGS with greater interlayer numbers has larger cavern deformation. The increase in interlayer thickness will improve the deformation resistance of caverns, but the effect is not obvious. The UGS with an interlayer dip angle of 12.5 deg has the best stability. Soft interlayer will decrease the deformation resistance of caverns, while hard interlayer has the opposite effect. In addition, the UGS stability can be enhanced by reducing the pressure difference between adjacent caverns. It is reasonable that the cavern spacing is twice the cavern diameter, which is beneficial to the UGS stability and will not cause a waste of salt rock resources. Finally, the corresponding production and construction control measures are discussed according to each factor's influence degree.

A High Efficiency MAC Protocol for WLANs: Providing Fairness in Dense Scenarios

Collisions are a main cause of throughput degradation in wireless local area networks. The current contention mechanism used in the IEEE 802.11 networks is called carrier sense multiple access with collision avoidance (CSMA/CA). It uses a binary exponential backoff technique to randomize each contender attempt of transmitting, effectively reducing the collision probability. Nevertheless, CSMA/CA relies on a random backoff that while effective and fully decentralized, in principle is unable to completely eliminate collisions, therefore degrading the network throughput as more contenders attempt to share the channel. To overcome these situations, carrier sense multiple access with enhanced collision avoidance (CSMA/ECA) is able to create a collision-free schedule in a fully decentralized manner using a deterministic backoff after successful transmissions. Hysteresis and fair share are two extensions of CSMA/ECA to support a large number of contenders in a collision-free schedule. CSMA/ECA offers better throughput than CSMA/CA and short-term throughput fairness. This paper describes CSMA/ECA and its extensions. In addition, it provides the first evaluation results of CSMA/ECA with non-saturated traffic, channel errors, and its performance when coexisting with CSMA/CA nodes. Furthermore, it describes the effects of imperfect clocks over CSMA/ECA and presents a mechanism to leverage the impact of channel errors and the addition/withdrawal of nodes over collision-free schedules. Finally, the experimental results on throughput and lost frames from a CSMA/ECA implementation using commercial hardware and open-source firmware are presented.

Throughput Maximization for Sensor-Aided Cognitive Radio Networks with Continuous Energy Arrivals.

We consider a Sensor-Aided Cognitive Radio Network (SACRN) in which sensors capable of harvesting energy are distributed throughout the network to support secondary transmitters for sensing licensed channels in order to improve both energy and spectral efficiency. Harvesting ambient energy is one of the most promising solutions to mitigate energy deficiency, prolong device lifetime, and partly reduce the battery size of devices. So far, many works related to SACRN have considered single secondary users capable of harvesting energy in whole slot as well as short-term throughput. In the paper, we consider two types of energy harvesting sensor nodes (EHSN): Type-I sensor nodes will harvest ambient energy in whole slot duration, whereas type-II sensor nodes will only harvest energy after carrying out spectrum sensing. In the paper, we also investigate long-term throughput in the scheduling window, and formulate the throughput maximization problem by considering energy-neutral operation conditions of type-I and -II sensors and the target detection probability. Through simulations, it is shown that the sensing energy consumption of all sensor nodes can be efficiently managed with the proposed scheme to achieve optimal long-term throughput in the window.

Exploiting Channel Memory for Joint Estimation and Scheduling in Downlink Networks—a Whittle’s Indexability Analysis

We study opportunistic multiuser scheduling in downlink networks with Markov-modeled outage channels. We consider the scenario that the scheduler does not have full knowledge of the channel state information, but instead estimates the channel state by exploiting the memory inherent in the Markov channels along with Automatic-Repeat-reQues-styled-styled feedback from the scheduled users. Opportunistic scheduling is optimized in two stages: 1) channel estimation and rate adaptation are performed to maximize the short-term throughput, i.e., the successful transmission rate of the scheduled user in the current slot and 2) user scheduling is performed, based on the short-term throughput, to maximize the overall long-term sum-throughput of the downlink. The scheduling problem is a partially observable Markov decision process with the classic exploitation versus exploration tradeoff that is difficult to quantify. We, therefore, study the problem in the framework of restless multiarmed bandit processes, and perform a Whittle’s indexability analysis. Whittle’s indexability is traditionally known to be hard to establish and the index policy derived based on Whittle’s indexability is known to have optimality properties in various settings. We show that the problem of downlink scheduling under imperfect channel state information is Whittle indexable and derive the Whittle’s index policy in closed form. Through extensive numerical experiments, we show that the Whittle’s index policy has near-optimal performance and is robust against various imperfections in channel state feedback. Our work reveals that, under incomplete channel state information, exploiting channel memory for opportunistic scheduling can result in significant system-level performance gains and that almost all of these gains can be realized using the polynomial-complexity Whittle’s index policy.

Throughput Maximization with Short-Term and Long-Term Jain's Index Constraints in Downlink OFDMA Systems

We aim to maximize system throughput subject to constraints on both short-term and long-term fairness in terms of Jain's index in single cell downlink OFDMA systems, where the transmission power is fixed. While it is accepted that short-term fairness implies long-term fairness, we find that this is not always true. Noting that long-term performance metric is the average of short-term ones, we point out that it depends on the averaging method and the fairness definition. We prove that short-term throughput Jain's index implies long-term throughput Jain's index. Therefore, we can remove the long-term fairness constraint if it is looser than the short-term constraint. Otherwise, we heuristically replace the long-term fairness constraint by a cumulative fairness constraint. We relax the considered discrete subchannel and slot allocation problem into a continuous convex problem, which can be efficiently solved. Then, the discrete resource allocation is derived by rounding the optimal solution. The analysis indicates that the rounding error is small. Simulation results show that we obtain a good suboptimal solution with small deviations from the optimal relaxed system throughput and the Jain's index constraints. Moreover, comparing with the strategies that take into account only long-term fairness, we guarantee both long-term and short-term fairness.

Optimal Scheduling and Power Allocation for Two-Hop Energy Harvesting Communication Systems

Energy harvesting (EH) has recently emerged as a promising technique for green communications. To realize its potential, communication protocols need to be redesigned to combat the randomness of the harvested energy. In this paper, we investigate how to apply relaying to improve the short-term performance of EH communication systems. With an EH source and a non-EH half-duplex relay, we consider two different design objectives: 1) short-term throughput maximization; and 2) transmission completion time minimization. Both problems are joint scheduling and power allocation problems, rendered quite challenging by the half-duplex constraint at the relay. A key finding is that directional water-filling (DWF), which is the optimal power allocation algorithm for the single-hop EH system, can serve as guideline for the design of two-hop communication systems, as it not only determines the value of the optimal performance, but also forms the basis to derive optimal solutions for both design problems. Based on a relaxed energy profile along with the DWF algorithm, we derive key properties of the optimal solutions for both problems and thereafter propose efficient algorithms. Simulation results will show that both scheduling and power allocation optimizations are necessary in two-hop EH communication systems.

Sum-rate optimal power policies for energy harvesting transmitters in an interference channel

This paper considers a two-user Gaussian interference channel with energy harvesting transmitters. Different than conventional battery powered wireless nodes, energy harvesting transmitters have to adapt transmission to availability of energy at a particular instant. In this setting, the optimal power allocation problem to maximize the sum throughput with a given deadline is formulated. The convergence of the proposed iterative coordinate descent method for the problem is proved and the short-term throughput maximizing offline power allocation policy is found. Examples for interference regions with known sum capacities are given with directional waterfilling interpretations. Next, stochastic data arrivals are addressed. Finally, online and/or distributed near-optimal policies are proposed. Performance of the proposed algorithms are demonstrated through simulations.

Optimum Transmission Policies for Battery Limited Energy Harvesting Nodes

Wireless networks with energy harvesting battery equipped nodes are quickly emerging as a viable option for future wireless networks with extended lifetime. Equally important to their counterpart in the design of energy harvesting radios are the design principles that this new networking paradigm calls for. In particular, unlike wireless networks considered to date, the energy replenishment process and the storage constraints of the rechargeable batteries need to be taken into account in designing efficient transmission strategies. In this work, such transmission policies for rechargeable nodes are considered, and optimum solutions for two related problems are identified. Specifically, the transmission policy that maximizes the short term throughput, i.e., the amount of data transmitted in a finite time horizon is found. In addition, the relation of this optimization problem to another, namely, the minimization of the transmission completion time for a given amount of data is demonstrated, which leads to the solution of the latter as well. The optimum transmission policies are identified under the constraints on energy causality, i.e., energy replenishment process, as well as the energy storage, i.e., battery capacity. For battery replenishment, a model with discrete packets of energy arrivals is considered. The necessary conditions that the throughput-optimal allocation satisfies are derived, and then the algorithm that finds the optimal transmission policy with respect to the short-term throughput and the minimum transmission completion time is given. Numerical results are presented to confirm the analytical findings.

Decision support systems for effective maintenance operations

To compete successfully in the market place, leading manufacturing companies are pursuing effective maintenance operations. Existing computerized maintenance management systems (CMMS) can no longer meet the needs of dynamic maintenance operations. This paper describes newly developed decision support tools for effective maintenance operations: (1) data-driven short-term throughput bottleneck identification, (2) estimation of maintenance windows of opportunity, (3) prioritization of maintenance tasks, (4) joint production and maintenance scheduling systems, and (5) maintenance staff management. Mathematical algorithms and simulation tools are utilized to illustrate the concepts of these decision support systems. Results from real implementations in automotive manufacturing are presented to demonstrate the effectiveness of these tools.

Throughput Optimization in High Speed Downlink Packet Access (HSDPA)

In this paper, we investigate single user throughput optimization in High Speed Downlink Packet Access (HSDPA). Specifically, we propose offline and online optimization algorithms which adjust the Channel Quality Indicator (CQI) used by the network for scheduling of data transmission. In the offline algorithm, a given target block error rate (BLER) is achieved by adjusting CQI based on ACK/NAK history. By sweeping through different target BLERs, we can find the throughput optimal BLER offline. This algorithm could be used not only to optimize throughput but also to enable fair resource allocation among multiple users in HSDPA. In the online algorithm, the CQI offset is adapted using an estimated short term throughput gradient without the need for a target BLER. An adaptive stepsize mechanism is proposed to track temporal variation of the environment. Convergence behavior of both algorithms is analyzed. The part of the analysis that deals with constant step size gradient algorithm may be applied to other stochastic optimization techniques. The convergence analysis is confirmed by our simulations. Simulation results also yield valuable insights on the value of optimal BLER target. Both offline and online algorithms are shown to yield up to 25% of throughput improvement over the conventional approach of targeting 10% BLER.

A Dynamic Utility Adaptation Framework for Efficient Multimedia Service Support in CDMA Wireless Networks

In this paper the problem of channel-aware opportunistic resource allocation for the downlink in CDMA wireless networks supporting simultaneously real-time multimedia and non-real-time data services is addressed. In order to treat different types of services with diverse QoS prerequisites through common optimization formulation a utility-based power and rate allocation framework is adopted. Emphasis is placed on real-time services' strict short-term QoS prerequisites, the fulfillment of which requires a significantly different treatment than the use of static utility functions, traditionally used to address long-term QoS or fairness prerequisites of delay-tolerant data services. To that end, we introduce a novel framework that enables the dynamic adaptation of real-time multimedia users' utilities as the system evolves, with respect to the corresponding short-term throughput service performance variations. The corresponding non-convex network utility maximization (NUM) problem is then formulated and solved, to obtain optimal power and rate allocation. Via simulation and analysis it is demonstrated that significant performance improvements are achieved in terms of real-time user's short-term throughput requirement satisfaction, without any considerable loss in total system throughput. Finally, essential tradeoffs between fulfilling real-time services' short-term QoS prerequisites and maximizing system performance, under an opportunistic scheduling wireless environment, are revealed and quantified.

Downlink scheduling with guarantees on the probability of short-term throughput

We consider the problem of scheduling multiple transmissions on the downlink of a wireless network with performance guarantees in the form of the probabilities that short term throughputs exceed user specified thresholds. Many interactive data applications have some degree of a latency requirement, and measure performance by throughput over a relatively short time interval. We refer to the fraction of time such user throughput reaches a predefined rate threshold or higher as tail probability. The problem is formulated as maximizing the minimum ratio of tail probability to the user specified probability threshold. We present necessary and sufficient optimality conditions for the case in which the time interval of interest is consistent with the time scale of channel variation. An online algorithm is proposed which can achieve the optimality. For the case in which the time interval of interest is large compared to the time scale of channel variation, we develop an online algorithm which attempts to maximize the minimum normalized tail probability by taking the advantage of channel variation over users and over time. Simulation results demonstrate that the proposed algorithm can achieve better performance than other algorithms such as the proportional fair algorithm and the Max C/I algorithm.

Utility Based Short-term Throughput Driven Scheduling Approach for Efficient Resource Allocation in CDMA Wireless Networks

In this paper, we address the problem of efficient recourse allocation in CDMA wireless networks supporting multimedia services with various and often diverse QoS prerequisites, placing special emphasis on real-time services' essential requirements satisfaction. We first provide an analytical framework for studying real-time users' short-term delay and throughput properties under fundamental opportunistic scheduling policies. The corresponding results demonstrate that probabilistic delay constraints are insufficient indicators of real-time services' QoS prerequisites, while probabilistic short-term throughput requirements are more appropriate in asserting their performance expectations. Based on these observations and results, we propose and develop a scheduling policy for efficiently supporting heterogeneous services that include delay-tolerant non-real-time and delay-sensitive real-time services, over a wireless CDMA system under a common utility based framework. A user's utility characterizes his degree of satisfaction for the received service as well as QoS expectations fulfillment, in a normalized way. Aiming at the maximization of users' utilities, both non-real-time services' long-term and real-time services' short-term throughput QoS requirements are met, under the proposed opportunistic scheduler. Finally, via modeling and simulation it is demonstrated that significant performance improvements concerning both types of services' QoS requirements satisfaction are achieved through the proposed scheduling approach.

Unisource and Multisource Tree Schemes for Collision Resolution in Wireless MAN

IEEE 802.16 wireless MAN standard specifies the air interface of broadband wireless access systems providing multiple services. In the wireless MAN, the best effort service class is ranked on the lowest position in priority and is assisted by a MAC scheme based on reservation ALOHA. In such a MAC scheme, a collision of resource requests is unavoidable so that the wireless MAN standard adopted a truncated binary exponential back-off scheme to arbitrate request attempts. While an exponential back-off scheme is simple to implement, its capture or starvation effect was revealed to deteriorate the fairness in short-term throughput and delay variance in the long term. Aiming at improving the throughput and delay performance, we thus propose the unisource and multisource m-ary tree schemes as alternatives for resolving request collisions in a wireless MAN. For the unisource tree scheme, we first develop an analytical method to exactly calculate the throughput in the saturated environment. Using the analytical method and simulation method as well, we then evaluate the saturated throughput, mean of MAC PDU delay and variance of MAC PDU delay in each proposed scheme. From the numerical examples, we confirm that the unisource and multisource m-ary tree schemes invoke superior throughput and delay performance to a truncated binary exponential back-off scheme.

Self-coordinating localized fair queueing in wireless ad hoc networks

Distributed fair queueing in a multihop, wireless ad hoc network is challenging for several reasons. First, the wireless channel is shared among multiple contending nodes in a spatial locality. Location-dependent channel contention complicates the fairness notion. Second, the sender of a flow does not have explicit information regarding the contending flows originated from other nodes. Fair queueing over ad hoc networks is a distributed scheduling problem by nature. Finally, the wireless channel capacity is a scarce resource. Spatial channel reuse, i.e., simultaneous transmissions of flows that do not interfere with each other, should be encouraged whenever possible. In this paper, we reexamine the fairness notion in an ad hoc network using a graph-theoretic formulation and extract the fairness requirements that an ad hoc fair queueing algorithm should possess. To meet these requirements, we propose maximize-local-minimum fair queueing (MLM-FQ), a novel distributed packet scheduling algorithm where local schedulers self-coordinate their scheduling decisions and collectively achieve fair bandwidth sharing. We then propose enhanced MLM-FQ (EMLM-FQ) to further improve the spatial channel reuse and limit the impact of inaccurate scheduling information resulted from collisions. EMLM-FQ achieves statistical short-term throughput and delay bounds over the shared wireless channel. Analysis and extensive simulations confirm the effectiveness and efficiency of our self-coordinating localized design in providing global fair channel access in wireless ad hoc networks.

Dynamic Operational Policies in an Automated Warehouse

Abstract In this paper we consider the optimal relocation of pallets with a high expectancy of retrieval within each storage rack of an automated warehouse to meet the fluctuating, short-term throughput requirements imposed on the automated storage-retrieval machines. The prepositioning of these pallets closer to the input/output point of each rack during off-peak periods will reduce the expected travel time for the storage/retrieval machines during future peak periods of the planning horizon. As the model has been abstracted from an actual operating environment, we first describe the environment in which the problem has been posed. We then exploit the special structure of the problem to develop conditions that an optimal relocation policy should satisfy. Based on these optimality conditions, we develop a very efficient optimal relocation algorithm. Finally, we present the performance of several relocation policies in the warehouse studied. Handled by the Department of Manufacturing and Automated Production.