Fast Learning in Spiking Neural Networks by Learning Rate Adaptation

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.

Throughput Analysis of IEEE 802.11 Multirate WLANs with Collision Aware Rate Adaptation Algorithm

This paper presents a mathematical model that analyzes the throughput of the IEEE 802.11b distributed coordination function （DCF） with the collision aware rate adaptation （CARA） algorithm. IEEE 802.11 WLANs provide multiple transmission rates to improve system throughput by adapting the transmission rate to the current channel conditions. The system throughput is determined by some stations using low transmission rates due to bad channel conditions. CARA algorithm does not disturb the existing IEEE 802.11b formats and it can be easily incorporated into the commercial wireless local area networks （WLAN） devices. Finally, we verify our findings with simulation.

Joint Optimization of Admission Control and Rate Adaptation for Video Sharing over Multirate Wireless Community Cloud

Emerging wireless community cloud enables usergenerated video content to be shared and consumed in a social context. However, the nature of shared wireless medium and timevarying channels seriously limits the quality of service(QoS), partially owing to the lack of mechanisms for effectively utilizing multi-rate channel resources. In this paper, the joint optimization of admission control and rate adaptation is proposed, resulting in a bandwidth-aware rate-adaptive admission control(BRAC) scheme to provide bandwidth guarantee for sharing social multimedia contents. The analytical approach leads to the following major contributions:(1) a bandwidth-aware rate selection(BRS) algorithm to optimally meet the bandwidth requirement of the data session and channel conditions at the physical layer;(2) a routing-coupled rate adaption and admission control algorithm to admit data sessions with bandwidth guarantee. Moreover, extensive numerical simulations suggest that BRAC is efficient and effective in meeting the bandwidth requirements for sharing social multimedia contents. These insights will shed light on communication system implementation for multimedia content sharing over multirate wireless community cloud.

Rate Adaptation for Decoding-and-Forward Relay Channel by Random Projections Codes

This paper investigates rate adaptation schemes for decoding-and-forward (DF) relay system based on random projections codes (RPC). We consider a classic three node relay system model, where relay node performs on half-duplex mode. Then, we give out receiving diversity relay scheme and coding diversity relay scheme, and present their jointly decoding methods. Furthermore, we discuss the performance of the two schemes with different power allocation coefficients. Simulations show that our relay schemes can achieve different gain with the help of relay node. And, we should allocate power to source node to just guarantee relay node can decode successfully, and allocate remain power to relay node as far as possible. In this way, this DF relay system not only achieves diversity gain, but also achieves higher and smooth spectrum efficiency.

Fairness Aware Rate Adaptation and Proportional Scheduling for IEEE 802.11 WLANs Using FSE

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

Delay-aware downlink beamforming with discrete rate adaptation for green cloud radio access network

With the popularity of variety delay-sensitive services, how to guarantee the delay requirements for mobile users （MUs） is a great challenge for downlink beamformer design in green cloud radio access networks （C-RANs）. In this paper, we consider the problem of the delay-aware downlink beamforming with discrete rate adaptation to minimize the power consumption of C-RANs. We address the problem via a mixed integer nonlinear program （MINLP）, and then reformulate the MINLP problem as a mixed integer second-order cone program （MI-SOCP）, which is a convex program when the integer variables are relaxed as continuous ones. Based on this formulation, a deflation algorithm, whose computational complexity is polynomial, is proposed to derive the suboptimal solution. The simulation results are presented to validate the effectiveness of our proposed algorithm.

Rate Adaptation Schemes for Different Service Requirements in the Code Division Multiple Access Environment

the of the important requirements of any candidate architecture for the future personal communication System is the flexibility to support several different service requirements. These services may require different bearer rates or different Ed/N0 requirements. This paper will propose two rate adaptation schemes which could be applied to the Code Division Multiple Access (CDMA) architecture for accepting different service requirements.'.

Downlink call admission control with power allocation and rate scheduling for IDMA-based multi-beam satellite systems

Considering the advantage of interleave-division multiple-access(IDMA) technique and the technical bottlenecks in the existing satellite systems,IDMA is introduced into satellite communication networks.To further validate the IDMA into satellite systems,an effective call admission control(CAC) is proposed to maximize the resource utilization.After establishing the multi-beam satellite system model based on variable spreading gain(VSG) IDMA,the power allocation scheme based on SINR evolution technique and transmission rate adaptation for nonreal time interactive traffic are designed as integrated parts of the CAC,working together to improve the system performance in terms of power efficiency and throughput.Further,the analysis and simulation results show that IDMA under the proposed scheme can provide better QoS,in terms of the blocking/dropping probability,outage probability as well as delay performance.

Left univentricular pacing for cardiac resynchronization therapy using rate-adaptive atrioventricular delay

Objective To evaluate left univentricular （LUV） pacing for cardiac resynchronization therapy （CRT） using a rate-adaptive atrioven- tricular delay （RAAVD） algorithm to track physiological atrioventricular delay （AVD）. Methods A total of 72 patients with congestive heart failure （CHF） were randomized to RAAVD LUV pacing versus standard biventricular （BiV） pacing in a 1 ： 1 ratio. Echocardiography was used to optimize AVD for both groups. The effects of sequential BiV pacing and LUV pacing with optimized A-V （right atrio-LV） delay using an RAAVD algorithm were compared. The standard deviation （SD） of the S/R ratio in lead VI at five heart rate （HR） segments （Rs/R-SD5）, defined as the ＂tracking index,＂ was used to evaluate the accuracy of the RAAVD algorithm for tracking physiological AVD. Results TheQRS complex duration （132 ± 9.8 vs. 138± 10ms, P 〈 0.05）, the time required for optimization （21 ±5 vs. 50±8min, P〈 0.001）, the mitral regurgitant area （1.9 ± 1.1 vs. 2.5 ± 1.3 em2, P 〈 0.05）, the interventricular mechanical delay time （60.7 ± 13.3 ms vs. 68.3 ± 14.2 ms, P 〈 0.05）, and the average annual cost （13,200 ± 1000 vs. 21,600 ± 2000 RMB, P 〈 0.001） in the RAAVD LUV pacing group were significantly less than those in the standard BiV pacing group. The aortic valve velocity-time integral in the RAAVD LUV pacing group was greater than that in the standard BiV pacing group （22.7 ± 2.2 vs. 21.4 ± 2.1 cm, P 〈 0.05）. The Rs/R-SD5 was 4.08 ± 1.91 in the RAAVD LUV pacing group, and was significantly negatively correlated with improved left ventricular ejection fraction （LVEF） （ALVEF, Pearson＇s r = -0.427, P = 0.009）, and positively correlated with New York Heart Association class （Spearman＇s r - 0.348, P 0.037）. Conclusions RAAVD LUV pacing is as effective as standard BiV pacing, can be more physiological than standard BiV pacing, and can de- crease the average annual cost of CRT.

RATE ADAPTIVE PROTOCOL FOR MULTIRATE IEEE 802.11 NETWORKS

In this paper, a rate adaptive protocol AMARF (Adaptive Multirate Auto Rate Fallback) for multirate IEEE 802.11 networks is proposed. In AMARF, each data rate is assigned a unique success threshold, which is a criterion to judge when to switch a rate to the next higher one, and the success thresholds can be adjusted dynamically in an adaptive manner according to the running conditions, such as packet length and channel parameters. Moreover, the proposed protocol can be implemented by software without any change to the current IEEE 802.11 standards. Simulation result shows that AMARF yields significantly higher throughput than other existing schemes including ARF and its variants, in various running conditions.

Energy efficiency aware dynamic rate and power adaptation in carrier sensing based WLANs under Rayleigh fading and shadowing

We consider the problem of energy efficiency aware dynamic adaptation of data transmission rate and transmission power of the users in carrier sensing based Wireless Local Area Networks(WLANs)in the presence of path loss,Rayleigh fading and log-normal shadowing.For a data packet transmission,we formulate an optimization problem,solve the problem,and propose a rate and transmission power adaptation scheme with a restriction methodology of data packet transmission for achieving the optimal energy efficiency.In the restriction methodology of data packet transmission,a user does not transmit a data packet if the instantaneous channel gain of the user is lower than a threshold.To evaluate the performance of the proposed scheme,we develop analytical models for computing the throughput and energy efficiency of WLANs under the proposed scheme considering a saturation traffic condition.We then validate the analytical models via simulation.We find that the proposed scheme provides better throughput and energy efficiency with acceptable throughput fairness if the restriction methodology of data packet transmission is included.By means of the analytical models and simulations,we demonstrate that the proposed scheme provides significantly higher throughput,energy efficiency and fairness index than a traditional non-adaptive scheme and an existing most relevant adaptive scheme.Throughput and energy efficiency gains obtained by the proposed scheme with respect to the existing adapting scheme are about 75%and 103%,respectively,for a fairness index of 0.8.We also study the effect of various system parameters on throughput and energy efficiency and provide various engineering insights.

DESIGN ISSUES FOR BIT RATE-ADAPTIVE 3R O/E/O TRANSPONDER IN INTELLIGENT OPTICAL NETWORKS

This paper reported the design and implementation of a bit rate adaptive Optical Electronic Optical(O/E/O)transponder accomplishing almost full data rate transparency up to 2.5 Gb/s with 3R(Reamplifying,Reshaping and Retiming)processing in electronic domain.Based on the chipsets performing clock recovery in several continuous bit rate ranges,a clock and data regenerating circuit self adaptive to the bit rate of input signal was developed.Key design issues were presented,laying stress on the functional building blocks and scheme for the bit rate adaptive retiming circuit.The experimental results show a good scalability performance.

Markov Based Rate Adaption Approach for Live Streaming over HTTP/2

Dynamic adaptive streaming over HTTP （DASH） has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data- pushing in HTFP/2 is emerging as a promising technology. For video live over HTTP/2, new challenges arise due to both low-delay and small buffer constraints. In this paper, we study the rate adaption problem over HTFP/2 with the aim to improve the quality of experience （QoE） of live streaming. To track the dynamic characteristics of the streaming system, a Markov-theoretical approach is employed. System variables are taken into account to describe the system state, by which the system transi- tion probability is derived. Moreover, we design a dynamic reward function considering both the quality of user experience and dynamic system variables. Therefore, the rate adaption problem is formulated into a Markov decision based optimization problem and the best streaming policy is obtained. At last, the effectiveness of our proposed rate adaption scheme is demonstrated by numerous experiment results.

Power and rate adaption in wireless communication systems with energy harvesting-based on soft decision decoding

In this paper,the online power control and rate adaptation for a wireless communication system with energy harvesting(EH)are investigated,in which soft decision decoding is adopted by the receiver.To efficiently utilize the harvested energy and maximize the actual achievable transmission rate under the constraints of the available channel codes and modulation schemes,the transmit power,code rate and modulation order are jointly optimized.The Lyapunov framework is used to transform the long-term optimization problem into a per time slot optimization problem.Since there is no theoretical formula for the error rate of soft decision decoding,the optimization problem cannot be solved analytically.A table to find the optimal modulation order and code rate under the different values of signal-to-noise ratio(SNR)is built first,and then a numerical algorithm to find the solution to the optimization problem is given.The feasibility and performance of the proposed algorithm are demonstrated by simulation.The simulation results show that compared with the algorithms to maximize the theoretical channel capacity,the proposed algorithm can achieve a higher actual transmission rate.

UEs Power Reduction Evolution with Adaptive Mechanism over LTE Wireless Networks

At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).

WORST SUBCARRIER AVOIDING WATER-FILLING SUBCARRIER ALLOCATION SCHEME FOR OFDM-BASED CRN

Efficient and reliable subcarrier power joint allocation is served as a promising problem in cognitive OFDM-based Cognitive Radio Networks (CRN). This paper focuses on optimal subcarrier allocation for OFDM-based CRN. We mainly propose subcarrier allocation scheme denoted as Worst Subcarrier Avoiding Water-filling (WSAW), which is based on Rate Adaptive (RA) criterion and three constraints are considered in CRN. The algorithm divides the assignment procedure into two phases. The first phase is an initial subcarrier allocation based on the idea of avoiding selecting the worst subcarrier in order to maximize the transmission rate; while the second phase is an iterative adjustment process which is realized by swapping pairs of subcarriers between arbitrary users. The proposed scheme could assign subcarriers in accordance with channel coherence time. Hence, real time subcarrier allocation could be implemented. Simulation results show that, comparing with the similar existing algorithms, the proposed scheme could achieve larger capacity and a near-optimal BER performance.

Design and validation of real-time dynamic spectrum management in OFDM-based HNPLC systems

Time-varying frequency selective attenuation and colored noises are unfavorable characteristics of power line communication(PLC) channels of the low voltage networks.To overcome these disadvantages,a novel real-time dynamic spectrum management(DSM) algorithm in orthogonal frequency division multiplexing(OFDM)-based high-speed narrow-band power line communication(HNPLC) systems is proposed,and the corresponding FPGA circuit is designed and realized.Performance of the proposed DSM is validated with a large amount of network experiments under practical PLC circumstance.As the noise in each narrow subcarrier is approximately Gaussian,the proposed DSM adopts the BER/SER expression formulized via the AWGN channel to provide a handy and universal strategy for power allocation.The real-time requirement is guaranteed by choosing subcarriers in group and employing the same modulation scheme within each transmission.These measures are suitable for any modulation scheme no matter the system criterion is to maximize data rate or minimize power/BER.Algorithm design and hardware implementation of the proposed DSM are given with some flexible and efficient conversions.The DSM circuit is carried out with Xilinx KC705.Simulation and practical experiments validate that the proposed real-time DSM significantly improves system performance.

Research on Key Technologies of Electronic Shelf Labels Based on LoRa

The demand for Electronic Shelf Labels(ESL),according to the Internet of Things(IoT)paradigm,is expected to grow considerably in the immediate future.Various wireless communication standards are currently contending to gain an edge over the competition and provide the massive connectivity that will be required by a world in which everyday objects are expected to communicate with each other.Low-Power Wide-Area Networks(LPWANs)are continuously gaining momentum among these standards,mainly thanks to their ability to provide long-range coverage to devices,exploiting license-free frequency bands.The main theme of this work is one of the most prominent LPWAN technologies,LoRa.The purpose of this research is to provide long-range,less intermediate node,less energy dissipation,and a cheaper ESL system.Much research has already been done on designing the LoRaWAN network,not capable to make a reliable network.LoRa is using different gateways to transmit the same data,collision,data jamming,and data repetition are expected.According to the transmission behavior of LoRa,50%of data is lost.In this paper,the Improved Backoff Algorithm with synchronization technique is used to decrease overlapping and data loss.Besides,the improved Adaptive Data Rate algorithm(ADR)avoids the collision in concurrently transmitted data by using different Spreading Factors(SFs).The allocation of SF has the main role in designing LoRa based network to minimize the impact of the intra-interference,cost function,and Euclidean distance.For this purpose,the K-means machine learning algorithm is used for clustering.The data rate model is using an intra-slicing technique based on Maximum Likelihood Estimation(MLE).The data rate model includes three critical communication slices,High Critical Communication(HCC),Medium Critical Communication(MCC),and Low Critical Communication(LCC),having the specified number of End devices(EDs),payload budget delay,and data rate.Finally,different combinations of gateways are used to build ESL for 200 electronic shelf labels.

Adaptive coherence estimator based on the Krylov subspace technique for airborne radar

A novel adaptive detector for airborne radar space-time adaptive detection （STAD） in partially homogeneous environments is proposed. The novel detector combines the numerically stable Krylov subspace technique and diagonal loading technique, and it uses the framework of the adaptive coherence estimator （ACE）. It can effectively detect a target with low sample support. Compared with its natural competitors, the novel detector has higher proba- bility of detection （PD）, especially when the number of the training data is low. Moreover, it is shown to be practically constant false alarm rate （CFAR）.

Improving the accuracy of heart disease diagnosis with an augmented back propagation algorithm

A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale and congenital heart disease). Momentum term, adaptive learning rate, the forgetting mechanics, and conjugate gradients method are introduced to improve the basic BP algorithm aiming to speed up the convergence of the BP algorithm and enhance the accuracy for diagnosis. A heart disease database consisting of 352 samples is applied to the training and testing courses of the system. The performance of the system is assessed by cross-validation method. It is found that as the basic BP algorithm is improved step by step, the convergence speed and the classification accuracy of the network are enhanced, and the system has great application prospect in supporting heart diseases diagnosis.