Rate Adaptation Algorithm Research Articles

Energy efficient robust scheduling of periodic sensor packets for discrete rate based wireless networked control systems

Wireless networked control systems (WNCSs) require the design of a robust scheduling algorithm that meets the stringent timing and reliability requirements of control systems, despite the limited battery resources of sensor nodes and adverse properties of wireless communication for delay and packet errors. In this article, we propose a robust delay and energy constrained scheduling algorithm based on the exploitation of the mostly pre-known periodic data generation nature of sensor nodes in control systems. We first formulate the joint optimization of scheduling, power control and rate adaptation for discrete rate transmission model, in which only a finite set of transmission rates are supported, as a Mixed-Integer Non-linear Programming problem and prove its NP-hardness. Next, we propose an optimal polynomial-time power control and rate adaptation algorithm for minimizing the transmission time of a node subset. We then design a novel polynomial-time heuristic scheduling algorithm based on first determining the concurrently transmitting node subsets and then distributing them uniformly over time by a modified Karmarkar-Karp algorithm. We demonstrate the superior performance of the proposed scheduling algorithm in terms of robustness, delay and runtime on the Low-Rate Wireless Personal Area Network (LR-WPAN) simulation platform, which we developed in network simulator-3 (ns3).

Efficient Holistic Control

Industrial automation is embracing wireless sensor-actuator networks (WSANs). Despite the success of WSANs for monitoring applications, feedback control poses significant challenges due to data loss and stringent energy constraints in WSANs. Holistic control adopts a cyber-physical system approach to overcome the challenges by orchestrating network reconfiguration and process control at run time. Fundamentally, it leverages self-awareness across control and wireless boundaries to enhance the resiliency of wireless control systems. In this article, we explore efficient holistic control designs to maintain control performance while reducing the communication cost. The contributions of this work are five-fold: (1) We introduce a holistic control architecture that integrates Low-power Wireless Bus (LWB) and two control strategies, rate adaptation and self-triggered control ; (2) We present heuristics-based and optimal rate selection algorithms for rate adaptation; (3) We design novel network adaptation mechanisms to support rate adaptation and self-triggered control in a multi-hop WSAN; (4) We build WCPS-RT, a real-time network-in-the-loop simulator that integrates MATLAB/Simulink and a physical WSAN testbed to evaluate wireless control systems; (5) We empirically explore the tradeoff between communication cost and control performance in holistic control approaches. Our studies show that rate adaptation and self-triggered control offer advantages in control performance and energy efficiency, respectively, in normal operating conditions. The advantage in energy efficiency of self-triggered control, however, may diminish under harsh physical and wireless conditions due to the cost of recovering from data loss and physical disturbances.

Adaptive Control of Nonlinear MIMO System With Orthogonal Endocrine Intelligent Controller.

In this article, a new intelligent hybrid controller is proposed. The controller is based on the combination of the orthogonal endocrine neural network (OENN) and orthogonal endocrine ANFIS (OEANFIS). The orthogonal part of the controller consists of Chebyshev orthogonal functions, which are used because of their recursive property, computational simplicity, and accuracy in nonlinear approximations. Artificial endocrine influence on the controller is achieved by introducing excitatory and inhibitory glands to the OENN part of the structure, in the form of postsynaptic potentials. These potentials provide a network with the capability of additional self-regulation in the presence of external disturbances. The intelligent structure is trained using a developed learning algorithm, which consists of both offline and online learning procedures: online learning for fitting OENN substructure and offline learning for adjusting OEANFIS parameters. The learning process is expanded by introducing the learning rate adaptation algorithm, which bases its calculations on the sign of the error difference. Finally, the proposed intelligent controller was experimentally tested for control of a nonlinear multiple-input-multiple-output two rotor aerodynamical system. During the test phase, an additional four related intelligent control logics and default PID-based controllers were used, and tracking performance comparisons were performed. The proposed controller showed notably better online results in comparison to other control algorithms. The major deficiencies of the structure are complexity and noticeably large training computation time, but these drawbacks can be neglected if tracking performances of a dynamical system are of the highest importance.

Heterogeneous Multiuser QoE Enhancement Over DASH in SDN Networks

Among metrics that highly affect video quality and quality of experience (QoE), we can cite the stalling and the visual quality caused by mobile network overhead. Moreover, Dynamic Adaptive Streaming over HTTP (DASH) is widely used for video streaming to adapt the video in the current network conditions and improve the bandwidth utilization. In fact, a good rate adaptation algorithm is a novel technology can easily ameliorate the user perception and the quality of video. Although, several researches innocently estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behavior can trains in inequity and could potentially lower QoE for all clients. In this paper, we propose a novel approach based on Software Defined Network and DASH in order to reduce the stalling events during the video that aims to fairly enhance the QoE of multiple competing clients in a shared network environment. To do this, we first propose architecture to improve the user perception for a single user and in a second point; we generalize the proposed method for all users demanding a video service. The performance evaluation is conducted by using network simulator and the obtained results show that our approach can give significant gain in terms of user satisfaction.

Performance analysis of 802.11n-based cognitive wireless system with joint rate and power adaptation

ABSTRACT Cognitive radio technology is a solution to the spectrum scarcity problem that exists in today’s wireless networks. It provides spectrum conscious paradigm. However, these wireless systems impose eccentric challenges because of dynamic spectrum availability and diverse quality of service requirements. This paper investigates the performance of the adaptive cognitive system in multi-user and multi-channel environment. Since the system has to work in such a hostile environment, to enhance the performance of the system, modified joint rate and power adaptation algorithm is proposed. The novel packet-based water-filling power adaptation (PBWFPA) algorithm proposed in this treatise defines power per packet. The channel environment is consisting of multi-path fading channels TGn-A (Throughput task group-A) and TGn-D (Throughput task group-D). The adaptation algorithm considers interweave scheme in the performance evaluation of the cognitive system. The transmitter adapts transmit parameters based on channel state information (CSI) and soft information regarding activities of the primary users in the cognitive environment. The transmit parameters are computed at the receiver end and fed to the transmitter through the feedback channel. The simulation results reflect significant improvement in the throughput of the system even in an opportunistic channel-sharing environment. Higher-quality channel selection ensures the enhancement in the throughput of the system.

Non-Stationary Bandit Strategy for Rate Adaptation With Delayed Feedback

Rate adaptation is an efficient mechanism to utilize the channel capacity by adjusting the modulation and coding scheme in a dynamic wireless environment. The channel feedback, such as acknowledgment/negative acknowledgment (ACK/NACK) messages or the channel measurement such as received signal strength indicator (RSSI) can be applied to the rate adaptation. Existing rate adaptation algorithms are mainly driven by heuristics. They can not achieve satisfactory transmission rates in the time-varying environment. In this paper, we focus on the rate adaptation problem in a time-division duplex (TDD) system. A multi-armed bandit (MAB) strategy is applied to learn the changes of the channel condition from both RSSI and ACK/NACK signals. A discounted upper confidence bound based rate adaptation (DUCB-RA) algorithm is proposed. We show that the performance of the proposed algorithm is converged to the optimal with mathematical proofs. Simulation results demonstrate that the proposed algorithm can adapt to the time-varying channel and achieve better transmission throughput compared to existing rate adaptation algorithms.

Spectral efficiency of adaptive OFDM systems over high mobility Nakagami-m fading channels

ABSTRACT In this paper, we investigate the impact of rate and power adaptation (RPA) and rate adaptation (RA) using square M-ary quadrature amplitude modulation (MQAM) on the average spectral efficiency (ASE) of orthogonal frequency division multiplexing (OFDM) systems under instantaneous bit-error-rate (BER) constraint over high mobility Nakagami multipath fading (NMF) channels. Especially, the closed-form expressions for the average spectral efficiency (ASE) of RPA and RA are obtained when these adaptive schemes are used over NMF channels with existence inter-carrier interference (ICI) caused by the high mobility. By comparing the ASE of RPA and RA under different number of subcarriers, a simplified rate and subcarriers bandwidth adaptation (RSBA) algorithm is obtained. Simulation results indicate that RSBA is more suitable for high-speed communication environment than RA.

Bit Rate Transcoding for High Efficiency Video Coding

High efficiency video coding (HEVC) has demonstrated a notable increase in compression performance and is taken as a successor to H.264/AVC. Efficient bit rate adaptation algorithms are required to contain the HEVC standard between real life community facilities. A present issue of bit rate transcoding is its high computational complexity which is related with the encoder of a cascaded pixel domain transcoder. This paper gives Top to Bottom (T2B) approach to reduce complexity by using different complexity schemes. Proposed approach is effective in reducing complexity in Coding Unit (CU) optimization level. Coding Unit has been analyzed in T2B Approach. While examining the coding unit information of the input video is turned to account for decreasing the number of evaluation and early terminate the process. For the Prediction Unit (PU) level the units are powerfully chosen contingent upon likelihood of Prediction Unit sizes and co-found input prediction partitioning. By utilizing this approach, complexity scalable bit rate transcoding has achieved. Machine learning approach can be used to control computational complexity. Additionally, the T2B strategy is able to gain a spread on trade-offs in transrating complexity and coding performance. Using T2B approach 15% encoding time saving is accomplished. From this scheme, for the less resolution video 27% time saving has achieved.

Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree Videos

The 360-degree video allows users to enjoy the whole scene by interactively switching viewports. However, the huge data volume of the 360-degree video limits its remote applications via network. To provide high quality of experience ( QoE ) for remote web users, this paper presents a tile-based adaptive streaming method for 360-degree videos. First, we propose a simple yet effective rate adaptation algorithm to determine the requested bitrate for downloading the current video segment by considering the balance between the buffer length and video quality. Then, we propose to use a Gaussian model to predict the field of view at the beginning of each requested video segment. To deal with the circumstance that the view angle is switched during the display of a video segment, we propose to download all the tiles in the 360-degree video with different priorities based on a Zipf model. Finally, in order to allocate bitrates for all the tiles, a two-stage optimization algorithm is proposed to preserve the quality of tiles in FoV and guarantee the spatial and temporal smoothness. Experimental results demonstrate the effectiveness and advantage of the proposed method compared with the state-of-the-art methods. That is, our method preserves both the quality and the smoothness of tiles in FoV, thus providing the best QoE for users.

State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

Nowadays unmanned aerial vehicles (UAVs) are being widely applied to a wealth of civil and military applications. Robust and high-throughput wireless communication is the crux of these UAV applications. Yet, air-to-ground links suffer from time-varying channels induced by the agile mobility and dynamic environments. Rate adaptation algorithms are generally used to choose the optimal data rate based on the current channel conditions. State-of-the-art approaches leverage physical layer information for rate adaptation, and they work well under certain conditions. However, the above protocols still have limitation under constantly changing flight states and environments for air-to-ground links. To solve this problem, we propose StateRate, a state-optimized rate adaptation algorithm that fully exploits the characteristics of UAV systems using a hybrid deep learning model. The key observation is that the rate adaptation strategy needs to be adjusted according to motion-dependent channel models, which can be reflected by flight states. In this work, the rate adaptation protocol is enhanced with the help of the on-board sensors in UAVs. To make full use of the sensor data, we introduce a learning-based prediction module by leveraging the internal state to dynamically store temporal features under variable flight states. We also present an online learning algorithm by employing the pre-trained model that adapts the rate adaptation algorithm to different environments. We implement our algorithm on a commercial UAV platform and evaluate it in various environments. The results demonstrate that our system outperforms the best-known rate adaptation algorithm up to 53% in terms of throughput when the velocity is 2-6~m/s.

Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud

The increasing popularity of mobile video streaming in wireless networks has stimulated growing demands for efficient video streaming services. However, due to the time-varying throughput and user mobility, it is still difficult to provide high quality video services for mobile users. Our proposed optimization method considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The mobile network and device parameters are estimated in order to deliver the best quality video for the mobile user. We develop a rate adaptation algorithm using Lyapunov optimization for multi-source multimedia content delivery to minimize the video rate switches and provide higher video quality. The multi-source manager algorithm is developed to select the best stream based on the path quality for each path. The node joining and cluster head election mechanism update the node information. As the proposed approach selects the optimal path, it also achieves fairness and stability among clients. The quality of experience feature metrics like bitrate level, rebuffering events, and bitrate switch frequency are employed to assess video quality. We also employ objective video quality assessment methods like VQM, MS-SSIM, and SSIMplus for video quality measurement closer to human visual assessment. Numerical results show the effectiveness of the proposed method as compared to the existing state-of-the-art methods in providing quality of experience and bandwidth utilization.

Rate-Adaptive Fog Service Platform for Heterogeneous IoT Applications

With the advancement of the Internet of Things (IoT) technologies, the number of heterogeneous IoT applications requiring a variety of resources and services is increasing dramatically. Recently, the introduction of fog computing has further unlocked the potential of real-time services within the IoT context. On the basis of fog architecture, we herein propose a novel rate-adaptive fog service platform aiming at heterogeneous services provisioning and optimized service rate allocation. By forming several service groups in the fog network in which each service could be adequately provisioned, service consumers would always benefit from the fact that the majority of services produced by the IoT applications are in their proximity and thus are delivered to the destination promptly. Taking advantage of the well-known network utility maximization (NUM) approach, a service rate-adaptive algorithm is developed to empower fog nodes working together to adjust service delivery rate dynamically. Throughout this process, the algorithm takes the current network condition and constraint into account to ensure the rate is calibrated in favor of providing satisfactory quality of service (QoS) to each service receiver at the same time. Compared to other resource allocation strategies that mainly focus on allocating resources for a single network service, our proposed platform is capable of not only dealing with both the elastic and inelastic services but also handling the abrupt network changes and converging back to the global optimum rapidly.

Rate Adaptation With Thompson Sampling in 802.11ac WLAN

Rate adaptation (RA) is an essential mechanism in 802.11 WLAN. In the latest 802.11ac protocol, there are two emerging problems that need to be addressed for the RA. First, 802.11ac supporting more rate selections requires more efforts to find the optimal rate. Finally, 802.11ac supports higher rate. The difference between optimal rate and non-optimal rate can be so great that non-optimal rate would severely deteriorate the throughput of the WLAN. In order to tackle these problems, we develop a novel RA algorithm termed rate adaptation with Thompson sampling (RATS) for stationary and non-stationary channel environments. In this algorithm, we first consider compacting the search space by removing some rates to accelerate the convergence of the algorithm. Moreover, inspired by multi-armed bandit problem, we design RA algorithm based on Thompson sampling. Simulation results demonstrate that the performance of the proposed RATS outperforms the existing method.

Performance Analysis of Bit Loading Algorithms for fault tolerant Submarine Communication

In this paper, performance metrics of rate adaptive and margin adaptive bit loading algorithms is simulated and analyzed to improve the data rate in undersea communication channel. In fault tolerant undersea communication, achieving higher data rate is a challenge. Bit loading algorithms have proven efficient with limited power budget. An optimal bit loading algorithm with low complexity and fast convergence for rate adaptive and margin adaptive problems with bit error rate (BER) constraints will increase the data rate and reliability of the system significantly. The simulation has been carried for submarine audio channel with a bandwidth of 0-20kHz considering ambient noise and thorps attenuation model for 16 subcarriers. Three different approaches such as Chow, Campello and Ant Colony Optimization are simulated for margin adaptive and rate adaptive criteria. Simulation results show that, for margin adaptive conditions Campello and ACO algorithms reduce energy by a factor of 71.50% and 13.05% with respect to Chow’s algorithm with an input of 90 bits. It was also observed that for rate adaptive conditions, Chow and ACO algorithms gave marginally increased bit rate compared to Campello algorithm with energy fixed at 8.35J. Campello and ACO algorithm execution time were reduced by a factor of 67% and 87.10% respectively as compared to Chow’s algorithm.

SDMAC: A Software-Defined MAC for Wi-Fi to Ease Implementation of Soft Real-Time Applications

In distributed control systems where devices are connected through Wi-Fi, direct access to low-level MAC operations may help applications to meet their timing constraints. In particular, the ability to timely control single transmission attempts on air, by means of software programs running at the user space level, eases the implementation of mechanisms aimed at improving communication timeliness and reliability. Relevant examples are deterministic traffic scheduling, seamless channel redundancy, rate adaptation algorithms, and so on. In this paper, a novel architecture is defined, we call SDMAC, which in its current embodiment relies on conventional Linux PCs equipped with commercial Wi-Fi adapters. Preliminary SDMAC implementation on a real testbed and its experimental evaluation showed that integrating this paradigm in existing protocol stacks constitutes a viable option, whose performance suits a wide range of applications characterized by soft real-time requirements.

QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Dynamic adaptive streaming over HTTP (DASH) is widely used for video streaming on mobile devices. Ensuring a good quality of experience (QoE) for mobile video streaming is essential, as it severely impacts both the network and content providers’ revenue. Thus, a good rate adaptation algorithm at the client end that provides high QoE is critically important. Recently, a segment size-aware rate adaptation (SARA) algorithm was proposed for DASH clients. However, its performance on mobile clients has not been investigated so far. The main contributions of this article are twofold: (1) We discuss SARA’s implementation for mobile clients to improve the QoE in mobile video streaming, one that accurately predicts the download time for the next segment and makes an informed bitrate selection, and (2) we developed a new parametric QoE model to compute a cumulative score that helps in fair comparison of different adaptation algorithms. Based on our subjective and objective evaluation, we observed that SARA for mobile clients outperforms others by 17% on average, in terms of the Mean Opinion Score, while achieving, on average, a 76% improvement in terms of the interruption ratio. The score obtained from our new parametric QoE model also demonstrates that the SARA algorithm for mobile clients gives a better QoE among all the algorithms.

Deadline and Buffer Constrained Knapsack Problem

In this paper, we formulate a problem that is a variant of the knapsack problem. Even though the problem is NP-hard in general, we consider a special case of the problem where the problem is in P. For this special case, the proposed algorithm is linear time complexity in the number of bins. The proposed framework is a generalization of the framework that has been used recently in the context of finding rate adaptation algorithms for video streaming.

Loss Differentiated Channel Aware Rate Adaptation for IEEE 802.11n Wireless Links

Rate adaptation in wireless network adapts the transmission rate to the estimated quality of the channel as a single rate is not suitable over large period of time. Key limitation of existing closed loop Rate Adaptation Algorithm is its slow speed of adaptation due to the analysis of several frames for a reliable judgment, especially when the error rate is low. This is detrimental to the real time multimedia streaming applications. Implementation of a closed loop system is challenging in heterogeneous, power constrained networks. Adaptation is affected by collisions, faded channel and hidden nodes, which contribute differently to loss of data. Absence of loss differentiation may increase the transmission time, congesting the network further. This paper proposes an open loop, per packet, Loss Differentiated-Channel Aware Rate Adaptation (LD-CARA) for IEEE 802.11n. It uses block acknowledgement and other inherent features of 802.11n to differentiate losses and optimize the system throughput. Signal to noise ratio of the received signal, the raw measurement of the link, is directly related to the bit/frame error rate in the link. Use of SNR measurements characterize the instantaneous channel behavior and make the design agile. The performance is evaluated using network simulator NS3, and compared prevalent adaptations. It is found that LD-CARA is a stable algorithm and improves network throughput in static, mobile and interfered channels; however node mobility has an adverse effect on the protocol and the dropping ratio of mobile nodes deteriorates in the congested environment and results for the same are comparable to other protocols.

An Interference-Aware Rate and Channel Adaptation Scheme for Dense IEEE 802.11n Networks

Rate adaptation, which dynamically chooses transmission rate provided at the physical layer according to the current channel conditions, is a fundamental resource management issue in IEEE 802.11 networks with the goal of maximizing the network throughput. Traditional rate adaptation algorithms for IEEE 802.11n networks do not consider the interference problem, which becomes much more serious due to the rapid deployment of IEEE 802.11n devices and large number of mobile terminals. In this paper, an interference-aware rate and channel adaptation scheme RaCA for intensive IEEE 802.11n networks was proposed. Firstly, RaCA leverages RSSI and CSI information together to measure the current channel conditions at the receiver side. RSSI is a coarse-grained indicator and CSI is a fine-grained indicator. Secondly, a two-stage rate adaptation scheme TSRA was designed, which can quickly adapt to optimal bit rate based on RSSI and CSI information. Finally, a quorum-based channel adaptation algorithm QCA was proposed, which does not need control channel. If channel suffers severe interferences, RaCA calls QCA to choose another channel to work on. Simulation and testbed implementation results demonstrate that RaCA achieves significant throughput gain over SampleLite and Minstrel-HT.

Evaluation of Congestion-Enabled Forwarding With Mixed Data Traffic in Vehicular Communications

ITS-G5 is a communication system for vehicle-to-everything communication for road safety and traffic efficiency applications. On the lower layers, it is based on IEEE 802.11 standard and uses a simple ad hoc mode with a random medium access control scheme. Ad hoc networking is realized by a geographical routing scheme that provides single- and multi-hop communication over ITS-G5 links for periodic and event-driven broadcast messages. Specifically, contention-based forwarding ( CBF ) allows for efficient and reliable multi-hop packet transport by overhearing and timer-based transmission control. To cope with the channel congestion, decentralized congestion control (DCC) adjusts the message rate and ensures that the network load keeps below a predefined threshold of the bandwidth. To enforce a node’s message rate, a “ Gatekeeper” above the MAC and beneath CBF is added. Under high network load, this Gatekeeper introduces an additional queuing delay seen by CBF , which can cause its overhearing function to fail. This contribution studies multi-hop forwarding in the context of DCC with the Gatekeeper and LIMERIC as a rate adaptation algorithm. We propose a congestion-enabled forwarding scheme that, in comparison to existing approaches considering DCC and forwarding separately, restores the efficient operation of CBF , and improves the communication performance in terms of reliability and latency for mixed data traffic composed of single- and multi-hop packets with different priorities. The simulations show the performance improvements in a freeway scenario. An analysis assesses the boundary conditions for CBF and the Gatekeeper. Moreover, the analysis corroborates the simulation.