Rate Adaptation Algorithm Research Articles

Joint Adaptive M-QAM Modulation and Power Adaptation for a Downlink NOMA Network

In this paper, we study joint adaptive M-QAM modulation and power adaptation for a downlink two-user non-orthogonal multiple access (NOMA) network. Without sacrificing bit error rate (BER), joint adaptive transmission can fully utilize the time-varying nature of wireless channels, by allowing both power and rate to adapt to channel fading. Two adaptive power allocation strategies, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scheme 1</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scheme 2</i> , each of which guarantees the minimum target rate for one user while supporting the highest possible rate for the other, are first proposed. Then, based on the two power schemes, the performance of joint adaptive transmission in terms of average spectral efficiency (SE) is studied for continuous-rate and discrete-rate modulation, while guaranteeing the minimum required rate and BER requirements. With the focus on practical discrete-rate M-QAM modulation, it is proved that for the strong user in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scheme 1</i> and the weak user in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scheme 2</i> , their average SEs converge to the minimum target rates. In order to further increase the total transmission rate, we then propose a dynamic rate and power adaptation (DRPA) algorithm, aiming to increase the rate of one user without sacrificing the rate of the other. It is shown that at high SNRs, the DRPA algorithm allows the strong user in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scheme 1</i> and the weak user in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Scheme 2</i> to continue to increase their transmission rates until reaching the highest modulation order that the system can support. Hence, the total transmission rate can be greatly increased at high SNRs due to the adoption of DRPA, by allowing both users in each scheme to reach the highest transmission rate in the system.

Online Teaching Wireless Video Stream Resource Dynamic Allocation Method considering Node Ability

At present, wireless network technology is advancing rapidly, and intelligent equipment is gradually popularized, which rapidly developed the mobile streaming media business. All kinds of mobile video applications have enriched people’s lives by carrying huge traffic randomly. Wireless networks (WNs) are facing an unprecedented burden, which allocates very important wireless video resources. Similarly, in WNs, the network status is dynamic and the terminal is heterogeneous, which causes the traditional video transmission system to fail to meet the needs of users. Hence, Scalable Video Coding (SVC) has been introduced in the video transmission system to achieve bit rate adaptation. However, in a strictly hierarchical traditional computer network, the wireless resource allocation strategy usually takes throughput as the only way to optimize the target, and it is terrible to make more optimizations for scalable video transmission. This article proposed a cross-layer design to enable information to be transmitted between the wireless base station and the video server to achieve joint optimization. To improve users’ satisfaction with video services, the wireless resource allocation problem and the video stream scheduling problem are jointly considered, which keep the optimization space larger. Based on the proposed architecture, we further study the design of wireless resource allocation algorithms and rate-adaptive algorithms for the scenario of multiuser transmission of scalable video in the Long-Term Evolution (LTE) downlink. Experimental outcomes have shown substantial performance enhancement of the proposed work.

Remote monitoring system for livestock environmental information based on LoRa wireless ad hoc network technology

The environmental quality of livestock houses is key to livestock breeding and directly affects the growth and health of animals. To target the characteristics of long application cycles and large coverage areas for environmental monitoring in large-scale livestock breeding, the current study designed a remote monitoring system to provide livestock environmental information based on LoRa wireless ad hoc network technology. The system consisted of collection terminals, control terminals, LoRa gateways, and Alibaba Elastic Compute Service. It realized real-time collection, wireless transmission, storage of multi-sensor node data, and remote control. The system was not limited by the selected time or region, because data interaction was achieved by accessing cloud servers using GPRS technology. Users could browse and obtain data from computers and a WeChat mini program from any location with network coverage. Additionally, the system used the improved receiver-based auto rate (RBAR) rate-adaptive algorithm in the LoRa wireless communication component. After application on a dairy farm, the results showed that the whole system collected 6140 sets of environmental data from four dairy houses. The packet loss rate was less than 1% within a communication distance of 604 m, and the communication success rate was greater than 99%. The control instructions were real-time and accurate, and the response time was less than 10 s, which met the remote control needs of large farms. The system provided powerful data and technical support for precision animal production. Keywords: remote monitoring, LoRa wireless ad hoc network, livestock environment, environment information, system DOI: 10.25165/j.ijabe.20221504.6708 Citation: Fu X, Shen W Z, Yin Y L, Zhang Y, Yan S C, Kou S L, et al. Remote monitoring system for livestock environmental information based on LoRa wireless ad hoc network technology. Int J Agric & Biol Eng, 2022; 15(4): 79–89.

An FPGA-based design for power efficient low delay rate adaptive pacemaker using accelerometer and heart rate sensor

This paper presents a power efficient, low delay and rate adaptive dual chamber pacemaker (PLRDPM) using heart rate and accelerometer sensor. In recent years, number of modifications have been done in the pacemaker design. However, design of an implantable device on an open source is still challenging. Through this paper, we are proposing a “proof of concept” for the design of PLRDPM on FPGA for improving the vital parameters: delay and power consumption. The proposed PLRDPM comprises of accelerometer and heart rate sensors to measure physical activity's effect on heart rate of the bradycardia patients. A rate adaptive pacing algorithm has been designed using two sensor’s data, to reduce the delay and power consumption. However, delay in the responses of various components in the circuitry produces an accumulative delay effect in any practical circuit. The delay and the power consumption for the proposed PLRDPM are found to be 2.82 ns and 9 mW, respectively.

Content Dependent Representation Selection Model for Systems Based on MPEG DASH

A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available literature that focus on video segmentation are mostly proprietary, use a high amount of computational power, lack the methodology, model notation, information needed for reproduction, or do not consider the spatial and temporal activity of video sequences. This paper presents a new model for selecting optimal parameters and number of representations for video encoding and segmentation, based on a measure of the spatial and temporal activity of the video content. The model was developed for the H.264 encoder, using Structural Similarity Index Measure (SSIM) objective metrics as well as Spatial Information (SI) and Temporal Information (TI) as measures of video spatial and temporal activity. The methodology that we used to develop the mathematical model is also presented in detail so that it can be applied to adapt the mathematical model to another type of an encoder or a set of encoding parameters. The efficiency of the segmentation made by the proposed model was tested using the Basic Adaptation algorithm (BAA) and Segment Aware Rate Adaptation (SARA) algorithm as well as two different network scenarios. In comparison to the segmentation available in the relevant literature, the segmentation based on the proposed model obtains better SSIM values in 92% of cases and subjective testing showed that it achieves better results in 83.3% of cases.

Content dependent spatial resolution selection for MPEG DASH segmentation

The need for standardization emerged with the fast development of video streaming technologies. MPEG Dynamic Adaptive Streaming over Hypertext Transfer Protocol (MPEG DASH) is a standard for adaptive video streaming. Applications developed in compliance with MPEG DASH ensure smooth playback to end-users while being hardware agnostic, cost-efficient, and easy to maintain. The client side of the MPEG DASH system houses the adaptation logic which has been investigated intensively over the last several years. The server side stores representation sets of video sequences as well as accompanying Media Presentation Description and initialization file. Compared to the client side, the server side has not been yet sufficiently explored.Although there are some papers investigating the parameter selection for representation sets, most solutions are proprietary, do not take into account the content, and do not give the comprehensive methodology and its notation that can be applied to other codecs and encoder parameters. Also, most of the currently available solutions are costly and require large computational power. This article provides a comprehensive analysis and methodology for selecting the most appropriate spatial resolution switching points, which define the bitrates where switching to the higher spatial resolution is optimal. Furthermore, it presents a spatial resolution selection model for the estimation of switching points, which considers the spatial and temporal activity of video sequences. With the developed model, the optimal representation selection process is simplified and the segmentation process in MPEG DASH is improved. The methodology proposed for the development of this content-dependent spatial resolution selection model can be used for encoders and coding parameters not covered by the presented research.Quality of the video sequences segmentation using the proposed switching points were tested using two different test cases for network conditions and two different adaptation algorithms: Basic Adaptation algorithm and Segment Aware Rate Adaptation algorithm. The streamed videos that used the proposed segmentation have achieved better SSIM (Structural Similarity Index) results in 83.33% of cases in comparison to the videos that used segmentation presented in the relevant literature.

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hard-coded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL’s wide deployment. Second, LoL’s objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> , which has been integrated to the official dash.js player in v3.2.0.

Channel Aware MAC Protocol with Rate Adaptation for MANET

In Mobile Adhoc networks there will be a transient nature of channel conditions. Rate control is the process of switching data rates dynamically based on channel conditions. We propose a rate adaption algorithm. The channel condition information is given as the input to the Rate selection algorithm. Now with the channel condition information, each node uses the rate selection algorithm to calculate the most energy-efficient setting of PHY rates for all links in its maximum interference range.

Stabilized adaptive sampling control for reliable real-time learning-based surveillance systems

In modern security systems such as CCTV-based surveillance applications, real-time deep-learning based computer vision algorithms are actively utilized for always-on automated execution. The real-time computer vision system for surveillance applications is highly computation-intensive and exhausts computation resources when it performed on the device with a limited amount of resources. Based on the nature of Internet-of-Things networks, the device is connected to main computing platforms with offloading techniques. In addition, the real-time computer vision system such as the CCTV system with image recognition functionality performs better when arrival images are sampled at a higher rate because it minimizes missing video frame feeds. However, performing it at overwhelmingly high rates exposes the system to the risk of a queue overflow that hampers the reliability of the system. In order to deal with this issue, this paper proposes a novel queue-aware dynamic sampling rate adaptation algorithm that optimizes the sampling rates to maximize the computer vision performance(i.e., recognition ratio) while avoiding queue overflow under the concept of Lyapunov optimization framework. Through extensive system simulations, the proposed approaches are shown to provide remarkable gains.

Learning Rate Optimization in CNN for Accurate Ophthalmic Classification

One of the most important hyper-parameters for model training and generalization is the learning rate. Recently, many research studies have shown that optimizing the learning rate schedule is very useful for training deep neural networks to get accurate and efficient results. In this paper, different learning rate schedules using some comprehensive optimization techniques have been compared in order to measure the accuracy of a convolutional neural network CNN model to classify four ophthalmic conditions. In this work, a deep learning CNN based on Keras and TensorFlow has been deployed using Python on a database that contains 1692 images, which consists of four types of ophthalmic cases: Glaucoma, Myopia, Diabetic retinopathy, and Normal eyes. The CNN model has been trained on Google Colab. GPU with different learning rate schedules and adaptive learning algorithms. Constant learning rate, time-based decay, step-based decay, exponential decay, and adaptive learning rate optimization techniques for deep learning have been addressed. Adam adaptive learning rate method. has outperformed the other optimization techniques and achieved the best model accuracy of 92.58% for training set and 80.49% for validation datasets, respectively.

Boosting Chirp Signal Based Aerial Acoustic Communication Under Dynamic Channel Conditions

Aerial acoustic communication attracts substantial attention for its simplicity and cost-effectiveness. Unfortunately, the preferred inaudible transmission has to strike a balance between the transmission rate and communication range, when the Bit-Error-Rate (BER) is under a certain threshold. Additionally, the performance of previous proposals can be deteriorated by dynamic channel conditions including near-far problem, device heterogeneity, and multipath fading. To this end, we propose a High-speed, long-range, and Robust Chirp Spread Spectrum (HRCSS) scheme for inaudible aerial acoustic communication under dynamic channels. HRCSS innovates in the definition of a loose orthogonality condition, and it leverages this orthogonality to overlap multiple chirp carriers in a single time duration to form a data symbol representing multiple bits, thereby substantially promoting the data rate. To further enhance system robustness in long communication ranges and dynamic channel conditions, we construct a lightweight rate adaptation algorithm and design a simple yet efficient normalization method. Experiment results reveal that HRCSS achieves a significant improvement in data rate over existing methods: it delivers 500 bps data rate with a BER of 0.24 percent at 10 m, and achieves 125 bps with zero BER at 20 m. Meanwhile, HRCSS can work adaptively under dynamic channel conditions while still retaining a BER below 3 percent.

Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems

With a target to maximize the throughput, a fast link rate adaptation algorithm for IEEE 802.11a/b/g/n/ac is proposed, which is basically preamble based and can adaptively compensate for the discrepancy between transmitter and receiver radio frequency performances by exploiting the acknowledgment signal. The target system is a 1 × 1 wireless local area network chip with no null data packet or sounding. The algorithm can be supplemented by automatic rate fallback at the initial phase to further expedite rate adaptation. The target system receives wireless channel coefficients and previous packet information, translates them to amended signal-to-noise ratios, and then, via the mean mutual information, selects the modulation and coding scheme with the maximum throughput. Extensive simulation and wireless tests are carried out to demonstrate the validity of the proposed adaptive preamble-based link adaptation in comparison with both the popular automatic rate fallback and ideal link adaptation. The throughput gain of the proposed link adaptation over automatic rate fallback is demonstrated over various packet transmission intervals and Doppler frequencies. The throughput gain of the proposed algorithm over ARF is 46% (15%) for a 1-tap (3-tap) channel over 10 m–250 m (16 m–160 m) normalized Doppler frequencies. Assuming a 3-tap channel and 30 m–50 m normalized Doppler frequencies, the throughput of the proposed algorithm is about 31 Mbps, nearly the same as that of ideal link adaptation, whereas the throughput of ARF is about 24 Mbps, leading to a 30% throughput gain of the proposed algorithm over ARF. The firmware is implemented in C and on Xilinx Zynq 7020 (Xilinx, San Jose, CA, USA) for wireless tests.

Energy-aware rate adaptation for computational RFID

Rate adaptation is an effective method to maximise the transmission goodput, especially for the passive sensing systems with a more variable channel environment and unstable energy states. Most existing approaches share three common drawbacks: excessive energy consumption, not in real-time, and not taking into account the energy of computational radio frequency identification (CRFID). We propose an energy-aware rate adaptation algorithm for CRFID, which use new channel detection methods, and design an adaptive time window algorithm, also, we establish a model of data rate and required energy, considered the impact of different data rates on energy consumption, and design a new CRFID runtime mechanism. We implement an algorithm on the wireless identification sensing platform (WISP) CRFIDs and commercial RFID reader, compared to the state-of-the-art scheme, the goodput gain and energy efficiency of our design are much improved.

A User Mobility-Based Rate Adaptation Approach for Dynamic HTTP Streaming

It is common for users to use smartphones to watch streaming videos when they are walking, taking a bus, or taking a subway. However, the current streaming rate adaptation algorithms are mostly based on the available network throughput or the player buffer occupancy, and the user mobility state is less considered. In order to improve the user experience quality under the mobile streaming media, we propose a user mobility-based rate adaptation approach, which classifies the user mobility state using the random forests algorithm and adopts different rate decision under different mobility states. The experimental results show that compared with popular approaches, our approach reduces the re-buffer events by 24% on average, and reduces the data traffic consumption by 19% on average.

The Impact of Rate Adaptation Algorithms on Wi-Fi-Based Factory Automation Systems.

Factory automation systems based on the IEEE 802.11 Wi-Fi standard may benefit from its Multi-Rate Support (MRS) feature, which allows for dynamically selecting the most suitable transmission rate for the targeted application context. The MRS is implemented by means of rate adaptation algorithms (RAAs), which has already demonstrated to be effective to improve both timeliness and reliability, which are typically strict requirements of industrial real-time communication systems. Indeed, some of such algorithms have been specifically conceived for reliable real-time communications. However, the computational complexity of such algorithms has not been effectively investigated yet. In this paper, we address such an issue, particularly focusing on the execution times of some specific rate adaptation algorithms, as well as on their impact on the automation tasks. In this respect, after a formal description of the algorithms, we present the outcomes of an extensive experimental session, which includes practical measurements and realistic simulations. The obtained results are encouraging, since the measured execution times indicate that rate adaptation algorithms can be profitably adopted by industrial automation systems, allowing for improving their reliability and timeliness without impacting on the overall performance.

Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks

Wireless technologies are being used in various applications for their ease of deployment and inherent capabilities to support mobility. Most wireless standards supports multiple data rates that may vary between few Mbps to few Gbps. Reaching the maximum supported data rate is what most application seek for. Nevertheless, the choice of data rates is very closely related to the quality of communication links and their stability. IEEE 802.11 standard introduced multi-rate support, since then, a lot of research has been done on rate adaptation, dealing with the different parameters that lead to an estimation of the channel conditions and the metrics that affect the network performance. In this paper, we present some of the popular rate adaptation schemes and summarize their characteristics. We categorize them as well into different categories according to their design and functionalities in terms of the strategies that are used to estimate channel conditions and decision making. We implemented some algorithms from the different categories in the network simulator NS-3 in order to evaluate their performance under different scenarios in Ad hoc and infrastructure modes. We present the lessons learned as well as our insights for future research work that can enhance the current approaches in the literature.

WiZig: Cross-Technology Energy Communication Over a Noisy Channel

The proliferation of IoT applications brings the demand of ubiquitous connections among heterogeneous wireless devices. Cross-Technology Communication (CTC) is a significant technique to directly exchange data among heterogeneous devices that follow different standards. By exploiting a side-channel like frequency, amplitude, or temporal modulation, the existing works enable CTC but have limited performance under channel noise. In this article, we propose WiZig, a novel CTC technique from WiFi to ZigBee that employs modulations in both the amplitude and temporal dimensions to optimize the throughput over a noisy channel. We establish a theoretical model of the energy communication channel to clearly understand the channel capacity. We then devise an online rate adaptation algorithm to adjust the modulation strategy according to the channel condition. Based on the theoretical model, WiZig controls the number of encoded energy amplitudes and the length of a receiving window, so as to optimize the CTC throughput. We implement a prototype of WiZig on a software radio platform and a commercial ZigBee device. The evaluation shows that WiZig achieves a throughput of 153.85bps with less than 1% symbol error rate in a real environment.

CARA-OHT: Collision-Aware Rate Adaptation for Optimal High-Throughput in IEEE 802.11s Wireless Mesh Networks

The wireless mesh network (WMN) is a future network technology that develops single-hop wireless local area networks (WLANs) into multi-hop wireless mesh networks, based on the IEEE 802.11s standard. However, this development still presents many challenges, such as determining the best route between sources and destinations, especially taking into account the use of the medium access control (MAC) and physical (PHY) layers of IEEE 802.11n/ac. Some papers have proposed rate adaptation algorithms particularly for single-hop networks; however, these only focused on mechanisms to find data rates suitable for channel conditions. In IEEE 802.11s WMNs, the design of the rate adaptation algorithm is more challenging. Besides considering the problem of channel quality and optimal throughput, it is necessary to also consider the problem of collision and the best route. It is important to take collision into account because the collision probability in multi-hop mesh networks is higher than that in single-hop networks and can lead to a lower throughput. Rate adaptation algorithms for IEEE 802.11s WMNs have been proposed in other papers, but they also do not consider the use of the MAC and PHY layers of IEEE 802.11n/ac. In this paper, we propose the collision-aware rate adaptation for optimal high-throughput (CARA-OHT) algorithm for WMN IEEE 802.11s. An evaluation through the extensive use of a network NS-3 simulator shows that the proposed algorithm exhibits a higher throughput than previously developed algorithms.

Performance evaluation of rate adaptation algorithms for seamless heterogeneous vehicular communications

VANET is an emerging area of wireless ad-hoc networks to contribute in the success of connected vehicles projects. The extremely changeable number of mobile nodes and high mobility are challenging issues. Furthermore, this particular network has several problems in term of defining suitable schemes and protocols like rate adaptation mechanisms. The overall performance of diverse applications in VANET such as traffic control and multimedia delivery is based on the achievement ratio these networks can offer and the network throughput. Rate adaptation is an essential technique to evade the performance network degradation and to maximize the throughput by using the estimation of the present channel characteristics and determining the optimal bitrate for subsequent transmissions. Despite there are several available rate control algorithms for 802.11 WLANs standards, there are few works devoted to the rate adaptation for the standard of vehicular networks. In this paper, we compare and evaluate the existing data rate adaptation schemes in numerous vehicular environments to recognize their behavior and analyze their performance in diverse scenarios. Six algorithms were chosen for comparison using the NS-3 simulator: AARF, AARF-CD, AMRR, CARA, Onoe, and Minstrel. The simulation outcomes demonstrate that Minstrel algorithm outperforms the remaining five mechanisms in dense and dynamic situations.

Look ahead to improve QoE in DASH streaming

When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.