Rate Adaptation Method Research Articles

Research on Rate Adaptation of Underwater Optical Communication with Joint Control of Photoelectric Domain

As the communication distance changes, the received signal strength of an underwater optical communication system will change, and the range of its variation may not only exceed the dynamic range of the photoelectric detection device but also cause the reliability of communication to change due to the change in the received signal-to-noise ratio. In order to maintain better communication over a longer distance, this paper proposes a rate-adaptive method for underwater optical communication with joint control in the photoelectric domain. In the optical domain, the incident light’s power is adaptively adjusted by controlling the transmittance of the liquid crystal light valve to reduce saturation distortion. In the electrical domain, the constellation distribution is optimized according to the desired probability mass function, and the modulation order is adjusted in real time by estimating the received signal-to-noise ratio of the link. The simulation results show that under the forward error correction (FEC) threshold, the proposed method increases the dynamic range of the photomultiplier tube (PMT) by about 10 dB and expands the dynamic range of the system’s communication distance.

Efficient Integration of Rate-Adaptive Reconciliation with Syndrome-Based Error Estimation and Subblock Confirmation for Quantum Key Distribution.

An effective post-processing algorithm is essential for achieving high rates of secret key generation in quantum key distribution. This work introduces an approach to quantum key distribution post-processing by integrating the three main steps into a unified procedure: syndrome-based error estimation, rate-adaptive reconciliation, and subblock confirmation. The proposed scheme employs low-density parity-check codes to estimate the quantum bit error rate using the syndrome information, and to optimize the channel coding rates based on the Slepian-Wolf coding scheme for the rate-adaptive method. Additionally, this scheme incorporates polynomial-based hash verification in the subblock confirmation process. The numerical results show that the syndrome-based estimation significantly enhances the accuracy and consistency of the estimated quantum bit error rate, enabling effective code rate optimization for rate-adaptive reconciliation. The unified approach, which integrates rate-adaptive reconciliation with syndrome-based estimation and subblock confirmation, exhibits superior efficiency, minimizes practical information leakage, reduces communication rounds, and guarantees convergence to the identical key. Furthermore, the simulations indicate that the secret key throughput of this approach achieves the theoretical limit in the context of a BB84 quantum key distribution system.

Multi-Sampling Rate Finite Control Set Model Predictive Control and Adaptive Method of Single-Phase Inverter

With the development of power switches and processor performance in recent years, the control frequency of inverters has been significantly improved. However, limited by technology and price, the sensor sampling frequency in large-scale industrial applications is much lower than the inverter control frequency that can be realized. This frequency mismatch limits the performance improvement of the inverter. In this article, the current and voltage at the non-sampling time are reconstructed using the current prediction control principle and the input observer theory, allowing a single-phase inverter to implement multi-sampling rate control with a low sampling frequency and high control frequency. In addition, an improved adaptive controller is designed to solve the effect of incorrect model parameters, which realizes adaptive control when the sampling frequency and control frequency are mismatched. Finally, the effectiveness of the method is verified through a simulation and experiments. The proposed method can solve the problem of high-speed switching for inverters under low-sampling-frequency conditions, improving the inverter’s adaptive performance and robustness.

Deep Reinforcement Learning-based Bitrate Adaptations in Dynamic Adaptive Streaming over HTTP

Dynamic adaptive streaming over HTTP (DASH) has been a superior video streaming technology in recent years. Bitrate adaptation function at video player plays a vital role in guaranteeing a high quality-of-experience for the users. This work evaluates the performance of several advanced deep reinforcement learning algorithms, i.e., deep Q-learning, actor-critic, and proximal policy optimization, applied in bitrate adaptations and compares them with other rate adaptation methods with real-trace datasets.

RAEN: Rate Adaptation for Effective Nodes in Backscatter Networks.

A backscatter network, as a key enabling technology for interconnecting plentiful IoT sensing devices, can be applicable to a variety of interesting applications, e.g., wireless sensing and motion tracking. In these scenarios, the vital information-carrying effective nodes always suffer from an extremely low individual reading rate, which results from both unpredictable channel conditions and intense competition from other nodes. In this paper, we propose a rate-adaptation algorithm for effective nodes (RAEN), to improve the throughput of effective nodes, by allowing them to transmit exclusively and work in an appropriate data rate. RAEN works in two stages: (1) RAEN exclusively extracts effective nodes with an identification module and selection module; (2) then, RAEN leverages a trigger mechanism, combined with a random forest-based classifier, to predict the overall optimal rate. As RAEN is fully compatible with the EPC C1G2 standard, we implement the experiment through a commercial reader and multiple RFID tags. Comprehensive experiments show that RAEN improves the throughput of effective nodes by 3×, when 1/6 of the nodes are effective, compared with normal reading. What is more, the throughput of RAEN is better than traditional rate-adaptation methods.

Efficient learning rate adaptation based on hierarchical optimization approach

This paper proposes a new hierarchical approach to learning rate adaptation in gradient methods, called learning rate optimization (LRO). LRO formulates the learning rate adaption problem as a hierarchical optimization problem that minimizes the loss function with respect to the learning rate for current model parameters and gradients. Then, LRO optimizes the learning rate based on the alternating direction method of multipliers (ADMM). In the process of this learning rate optimization, LRO does not require any second-order information and probabilistic model, so it is highly efficient. Furthermore, LRO does not require any additional hyperparameters when compared to the vanilla gradient method with the simple exponential learning rate decay. In the experiments, we integrated LRO with vanilla SGD and Adam. Then, we compared their optimization performance with the state-of-the-art learning rate adaptation methods and also the most commonly-used adaptive gradient methods. The SGD and Adam with LRO outperformed all the competitors on the benchmark datasets in image classification tasks.

The Adaptive Personalized Federated Meta-Learning for Anomaly Detection of Industrial Equipment

Federated meta-learning solves many challenges for industrial equipment anomaly detection, such as small-samples problems and significant heterogeneity in the equipment environment. However, this method is very sensitive and not easy to manually adjust an appropriate parameter for excellent results. To address this challenge, this paper proposes an adaptive personalized federated meta-learning framework. Specifically, we first design the hyperparameters online learning rate adaptation method to adjust hyperparameters dynamically. Then, we design a training mechanism with an adaptive learning rate based on model-agnostic meta-learning to update local meta-learning models adaptively. We validate our framework in a real anomaly dataset with a variable control approach. The experiments show that the value of the loss function hardly changes with differ learning rates α, and it can guarantee the accuracy to fluctuate within 1%. Even when α is 0, the model still has a good performance. Therefore, our framework has an excellent ability to adjust the hyperparameters. This framework can be used for anomaly detection of industrial equipment in different environments.

Creation of physiologic rhythm by closed loop stimulation in heart failure patients with severe chronotropic incompetence: worldwide first results of a pilot study

Abstract Background The prevalence of chronotropic incompetence (CI) in heart failure (HF) population is high and negatively impacts prognosis. In HF patients with an implanted cardiac resynchronisation therapy (CRT) device and severe CI, the effect of rate adaptive pacing on patient outcomes is unclear. Closed loop stimulation (CLS) based on cardiac impedance measurement may be an optimal method of heart rate adaptation according to metabolic need in HF patients with severe CI. Purpose This is the first study evaluating the effect of CLS on the established prognostic parameters assessed by the cardio-pulmonary exercise (CPX) testing and on quality of life (QoL) of the patients. Methods A randomised, controlled, double-blind and crossover pilot study has been performed in CRT patients with severe CI defined as the inability to achieve 70% of the age-predicted maximum heart rate (APMHR). After baseline assessment, patients were randomised to either DDD-CLS pacing (group 1) or DDD pacing at 40 bpm (group 2) for a 1-month period, followed by crossover for another month. At baseline and at 1- and 2-month follow-ups, a CPX was performed and QoL was assessed using the EQ-5D-5L questionnaire. The main endpoints were the effect of CLS on ventilatory efficiency (VE) slope (evaluated by an independent CPX expert), the responder rate defined as an improvement (decrease) of the VE slope by at least 5%, percentage of maximal predicted heart rate reserve (HRR) achieved, and QoL. Results Of the 36 patients enrolled in the study, 20 fulfilled the criterion for severe CI and entered the study follow-up (mean age 68.9±7.4 years, 70% men, LVEF=41.8±9.3%, 40%/60% NYHA class II/III). Full baseline and follow-up datasets were obtained in 17 patients. The mean VE slope and HRR at baseline were 34.4±4.4 and 49.6±23.8%, respectively, in group 1 (n=7) and 34.5±12.2 and 54.2±16.1% in group 2 (n=10). After completing the 2-month CPX, the mean difference between DDD-CLS and DDD-40 modes was −2.4±8.3 (group 1) and −1.2±3.5 (group 2) for VE slope, and 17.1±15.5% (group 1) and 8.7±18.8% (group 2) for HRR. Altogether, VE slope improved by −1.8±2.95 (p=0.31) in DDD-CLS versus DDD-40, and HRR improved by 12.9±8.8% (p=0.01). The VE slope decreased by ≥5% in 47% of patients (“responders to CLS”). The mean difference in the QoL between DDD-CLS and DDD-40 was 0.16±0.25 in group 1 and −0.01±0.05 in group 2, resulting in an overall increase by 0.08±0.08 in the DDD-CLS mode (p=0.13). Conclusion First results of the evaluation of the effectiveness of CLS in CRT patients with severe CI revealed that CLS generated an overall positive effect on well-established surrogate parameters for prognosis. About one half of the patients showed CLS response in terms of improved VE slope. In addition, CLS improved quality of life. Further clinical research is needed to identify predictors that can increase the responder rate and to confirm improvement in clinical outcomes. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Biotronik SE & Co. KG

QoE-Fair DASH Video Streaming Using Server-side Reinforcement Learning

To design an optimal adaptive video streaming method, video service providers need to consider both the efficiency and the fairness of the Quality of Experience (QoE) of their users. In Reference [8], we proposed a server-side QoE-fair rate adaptation method that considers both efficiency and fairness of the QoE. The server uses Reinforcement Learning (RL) to select a bitrate for each client sharing the same bottleneck link to the server in a way that achieves fairness among concurrent DASH clients and imposes that bitrate by dynamically modifying the client’s Media Presentation Description (MPD) file. In this article, we extend that work to minimize the number of actions the server needs to take to keep the system in its equilibrium state. By incorporating a Recurrent Neural Network, specifically an LSTM model, we modify the server’s training algorithm to achieve improvements in both the quality and the quantity of actions the server takes to guide the client. Performance evaluation of the modified algorithm for clients running both homogeneous and heterogeneous adaptation algorithms showed that the number of server actions dropped by 14% and 22%, respectively, while QoE-fairness improved by at least 6% and 10%, respectively.

Seamless rate adaptation for indoor visible light communication without CSI at the transmitter

Visible light communication (VLC), which uses LEDs for both lighting and communication purposes, has recently emerged as an alternative or support system to radio frequency (RF) communication. VLC channel between LEDs and photo-diodes is highly variable and pretty much depends on optical line of sight (LOS) and field of view (FOV). Therefore, received signal strength may vary dramatically, especially for mobile receivers. To ensure spectral efficient communication, it is essential to use rate adaptation methods. In this study, we propose rateless codes based rate adaptation method that adjusts transmission rate at receiver side and does not require channel state information (CSI) at the transmitter side. The spectral efficiency of the proposed method is analyzed and compared with adaptive modulation and coding (AMC) rate adaptation method widely used in literature. For analyses, we consider a realistic communication system model by using commercially available LEDs and also fulfilling illumination requirements. In order to minimize the multi-path effects, optical orthogonal frequency-division multiplexing (OFDM) is utilized. Numerical results indicate that proposed rate adaptive method can achieve seamless rate adaptation and has higher spectral efficiency compared to AMC in the presence of significant signal quality variations caused by mobility.

An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTP

Rate adaptation is one of the most important issues in dynamic adaptive streaming over HTTP (DASH). Due to the frequent fluctuations of the network bandwidth and complex variations of video content, it is difficult to deal with the varying network conditions and video content perfectly by using a single rate adaptation method. In this paper, we propose an ensemble rate adaptation framework for DASH, which aims to leverage the advantages of multiple methods involved in the framework to improve the quality of experience ( QoE ) of users. The proposed framework is simple yet very effective. Specifically, the proposed framework is composed of two modules, i.e., the method pool and method controller. In the method pool, several rate adaptation methods are integrated. At each decision time, only the method that can achieve the best QoE is chosen to determine the bitrate of the requested video segment. Besides, we also propose two strategies for switching methods, i.e., InstAnt Method Switching, and InterMittent Method Switching, for the method controller to determine which method can provide the best QoE s. Simulation results demonstrate that, the proposed framework always achieves the highest QoE for the change of channel environment and video complexity, compared with state-of-the-art rate adaptation methods.

Two-phase rate adaptation strategy for improving real-time video QoE in mobile networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience (QoE) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video QoE. First, to measure and assess video QoE, we provide a continuous QoE prediction engine modeled by RNN recurrent neural network. Different from traditional QoE models which consider the QoE-aware factors separately or incompletely, our RNN-QoE model accounts for three descriptive factors (video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-QoE can follow the subjective QoE quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time QoE compared with classical rate adaption methods.

Dual-Rate Adaptive Control for Mixed Separation Thickening Process Using Compensation Signal Based Approach

The mixed separation thickening process (MSTP) of hematite beneficiation is a strong nonlinear cascade process with frequency of slurry pump as input, underflow slurry flow-rate (USF) as inner-loop output, and underflow slurry density (USD) as outer-loop output. The model parameters such as settling velocity of slurry particles and slurry height are unknown and nonlinear. Moreover, these model parameters vary from flotation middling, sewage, and magnetic separation slurry. In this paper, the unknown change of the above dynamic characteristics are described by the previous sample unmodeled dynamics and its change rate. A novel adaptive controller using compensation signal based approach is developed. Inner-loop closed-loop control system equation and lifting technology are adopted to develop dual rate adaptive control method. Two compensation signals are constructed and added onto the linear proportional-integral (PI) controller. Such two compensation signals aim at eliminating the effects of the previous sample unmodeled dynamics and tracking error, respectively. The stability and convergence analysis is given and a simulation experiment on hardware-in-the-loop simulation system of MSTP based on industrial data is carried out, where it shows that the USD, USF, and its changing rate can be controlled well inside their targeted ranges when the system is subjected to unknown variations of its parameters.

AdaRate: A Rate-Adaptive Traffic Measurement Method in Software Defined Networks

AdaRate: A Rate-Adaptive Traffic Measurement Method in Software Defined Networks

Simulation and experimental testbed for adaptive video streaming in ad hoc networks

This paper presents a performance evaluation of the scalable video streaming over mobile ad hoc networks. In particular, we focus on the rate-adaptive method for streaming scalable video (H.264/SVC). For effective adaptation a new cross-layer routing protocol is introduced. This protocol provides an efficient algorithm for available bandwidth estimation. With this information, the video source adjusts its bit rate during the video transmission according to the network state. We also propose a free simulation framework that supports evaluation studies for scalable video streaming. The simulation experiments performed in this study involve the transmission of SVC streams with Medium Grain Scalability (MGS) as well as temporal scalability over different network scenarios. The results reveal that the rate-adaptive strategy helps avoid or reduce the congestion in MANETs obtaining a better quality in the received videos. Additionally, an actual ad hoc network was implemented using embedded devices (Raspberry Pi) in order to assess the performance of the proposed adaptive transmission mechanism in a real environment. Additional experiments were carried out prior to the implementation with the aim of characterizing the wireless medium and packet loss profile. Finally, the proposed approach shows an important reduction in energy consumption, as the study revealed.

Routing, power control and rate adaptation: A Q-learning-based cross-layer design

Mobile ad hoc networks (MANETs) contain individual nodes exchanging data in a distributed manner. Although each node is capable of determining its own transmission power level, rate adaptation method and routing strategy, few existing routing protocols fully explore the potential performance improvement by the joint optimization of power, rate and routing. This paper proposes and evaluates a cross-layer routing protocol considering power control and rate adaptation by using a delay-based non-selfish cost function with a multi-agent Q-learning coordination mechanism. A simulation-based evaluation, considering node density, node mobility, traffic load and flow number, shows that our algorithm improves the average end-to-end latency and throughput with acceptable power consumption level.

Performance Evaluation of Scalable Video Streaming in Mobile Ad hoc Networks

The development of video streaming services on wireless ad hoc networks is a challenge task as a consequence of different limitations such as bandwidth-constrained, variable capacity links and energy-constrained operation. Moreover, the dynamic topology of nodes causes frequent link failures and high error rates. We propose in this paper a performance evaluation of the scalable video streaming over mobile ad hoc networks. In particular, we focus on the rate-adaptive strategy for streaming scalable video (H.264/SVC). In order to provide QoS mechanisms in the routing process, a new routing protocol is introduced. This protocol estimates the available bandwidth value, which is sent to video source in order to adapt the bit rate during the video transmission. We also propose a simulation framework that supports evaluation studies for scalable video streaming. In the simulation experiments, SVC streams with combined scalability (quality and temporal scalability) were used. As quality scalability method, we used Medium Grain Scalability (MGS). The results reveal that the rate-adaptive method helps avoid or reduce the congestion in MANETs obtaining a better quality in the received videos.

A rate adaptive control method for Improving the imaging speed of atomic force microscopy

A simple rate adaptive control method is proposed to improve the imaging speed of the atomic force microscope (AFM) in the paper. Conventionally, the probe implemented on the AFM scans the sample surface at a constant rate, resulting in low time efficiency. Numerous attempts have been made to realize high-speed AFMs, while little efforts are put into changing the constant-rate scanning. Here we report a rate adaptive control method based on variable-rate scanning. The method automatically sets the imaging speed for the x scanner through the analysis of the tracking errors in the z direction at each scanning point, thus improving the dynamic tracking performance of the z scanner. The development and functioning of the rate adaptive method are demonstrated, as well as how the approach significantly achieves faster scans and a higher resolution AFM imaging.

Analysis and experimental evaluation of rate adaptation with transmit buffer information

In hardware, packet loss may happen due to overflow at a finite-depth transmit buffer in addition to the packet corruption in the channel. To reduce such losses and further improve spectral efficiency via rate selection, we exploit either statistical or instantaneous knowledge of transmit buffer occupancy and source packet distribution in IEEE 802.11-based systems, which have highly variable frame durations. We consider a traditional method of rate adaptation based on channel quality information and evaluate the throughput gain in hardware when the buffer occupancy and source packet distribution information are known. Our optimization objective is to maximize the throughput with constant transmit power since most wireless standards (e.g., 802.11, Bluetooth, ZigBee) operate in this manner. We study both cases with and without probe packets during the transmission. By evaluating the effect of diverse buffer sizes with different packet arrival distributions, both our theoretical analysis and our experimental results show that the throughput can be improved as much as 35% when the source packet distribution and buffer status information are exploited.

A Control-Theoretic Approach to Rate Adaption for DASH Over Multiple Content Distribution Servers

Recently, dynamic adaptive streaming over HTTP (DASH) has been widely deployed on the Internet. However, the research about DASH over multiple content distribution servers (MCDS-DASH) is limited. Compared with traditional single-server DASH, MCDS-DASH is able to offer expanded bandwidth, link diversity, and reliability. It is, however, a challenging problem to smooth video bitrate switching over multiple servers due to their diverse bandwidths. In this paper, we propose a block-based rate adaptation method considering both the diverse bandwidths and feedback buffered video time. In our method, multiple fragments are grouped into a block and the fragments are downloaded in parallel from multiple servers. We propose to adapt video bitrate at the block level rather than at the fragment level. By dynamically adjusting the block length and scheduling fragment requests to multiple servers, the requested video bitrates from the multiple servers are synchronized, making the fragments download in an orderly way. Then, we propose a control-theoretic approach to select an appropriate bitrate for each block. By modeling and linearizing the rate adaption system, we propose a novel proportional-derivative controller to adapt video bitrate with high responsiveness and stability. Theoretical analysis and extensive experiments on our network testbed and the Internet demonstrate the good efficiency of the proposed method.