ANCシステムにおけるオンライン2次経路とフィードバック経路モデリングのための補助ノイズ電力スケジューリングに関する研究

The idea of cancelling the acoustic noise by generating an anti-noise signal is very fascinating, and was first proposed by P. Lueg in 1936. In feedforward active noise control (ANC) systems, the anti-noise signal is generated with the help of reference and error microphones, an adaptive filtered-x-LMS (FxLMS) algorithm based ANC filter, and an electro-acoustic path named as the secondary path. For stable operation of ANC systems, the FxLMS algorithm needs an estimate of the secondary path. The anti-noise signal generated by the loudspeaker (part of secondary path) causes interference with the reference microphone signal. This interference is due to the presence of electro-acoustic path, named as feedback path, between the loudspeaker and the reference microphone. It is required to neutralize the effect of this feedback path, and hence an estimate of the feedback path is required. For online modeling of the secondary and feedback paths, an additional auxiliary noise is injected. This auxiliary noise contributes to the residual error, and thus degrades the noise-reduction-performance (NRP) of ANC system. In order to improve the NRP, a gain scheduling strategy is used to vary the variance of the injected auxiliary noise. The purpose of the gain scheduling is that when the model estimates of the secondary and the feedback paths are far from the actual unknown paths, auxiliary noise with large variance is injected. Once the model estimates are closer to the actual unknown paths, the variance of auxiliary noise is reduced to a small value. In this way, on one hand the gain scheduling can help us to achieve the required model estimates of secondary and feedback paths, and on the other hand to improve the NRP at the steady-state. In this thesis, we discuss the two most important issues, i.e., 1) online secondary path modeling (OSPM), and 2) online feedback path modeling and neutralization (FBPMN) with gain scheduling. In chapter 1, the basic underlying physical principle and configurations of active noise control (ANC) systems are explained. The application of the basic building block of an ANC system i.e. An adaptive filter, in different system identification scenarios is discussed. The most popular adaptive algorithm for ANC system, i.e., FxLMS algorithm is derived for the general secondary path. A brief overview is given for the two fundamental issues in ANC systems, i.e., 1) OSPM and 2) online FBPMN. The use of optimal excitation signal, i.e., Perfect sweep signals for system identification is described. In chapter 2, the existing methods for OSPM without gain scheduling, where the auxiliary noise with fixed variance is used in all operating conditions, are discussed. In this chapter a simplified structure for OSPM with the modified FxLMS (MFxLMS) adaptive algorithm is proposed. The advantage of the simplified structure is that it reduces the computational complexity of the MFxLMS algorithm based OSPM without having any compromise on the performance of ANC system. In chapter 3, the existing methods for OSPM with gain scheduling are discussed. The drawbacks with the existing gain scheduling strategies are highlighted, and some new gain scheduling strategies are proposed to improve the modeling accuracy of SPM filter and the NRP of an ANC system. In existing methods, the gain is varied based on the power of residual error signal which carries information only about the convergence status of ANC system. In the Proposed methods the gain is varied based on the power of error signal of SPM filter. This is more desirable way of controlling the gain because the power of error signal of SPM filter carries information about the convergence status of both the ANC system and the SPM filter. The performance comparison is carried out through the simulation results. In chapter 4, the second most important issue associated with the feedforward configuration of ANC system, i.e., the issue of online FBPMN is deal with. In the first part, the existing methods for online FBPMN without gain scheduling are discussed. A new structure is proposed for online FBPMN without gain scheduling. The performance of the existing methods is compare with the proposed method through the simulation results. In the new structure the good features from the existing structures are combined together. The predictor is used in the new structure to remove the predictable interference term from the error signal of adaptive FBPMN filter. In addition to this, the action of FBPM filter and the FBPN filter is combined into a single FBPMN filter. The advantage of the new structure over the existing structures is that it can better neutralize the effect of feedback coupling on the input signal of ANC filter, thus improves the convergence of ANC system. In the second part, a gain scheduling strategy is proposed to improve the NRP of ANC system. In addition to this, a self-tuned ANP scheduling strategy with matching step-size for FBPMN filter is also proposed that requires no tuning parameters and further improves the NRP of ANC systems. In chapter 5, the concluding remarks and some future research directions are given.電気通信大学201

An Efficient & Less Complex Solution to Mitigate Impulsive Noise in Multi-Channel Feed-Forward ANC System with Online Secondary Path Modeling (OSPM)

This paper deals with impulsive noise (IN) in multichannel (MC) Active Noise Control (ANC) Systems with Online Secondary Path Modelling (OSPM) employing adaptive algorithms for the first time. It compares performance of various existing techniques belonging to varied computational complexity range and proposes four new methods, namely: FxRLS-VSSLMS, VSSLMS-VSSLMS, FxLMAT-VSSLMS and NSS MFxLMAT-VSSLMS to deal with modest to very high impulsive noise (IN). Simulation results show that these proposed methods demonstrated improved performance in terms of fast convergence speed, lowest steady state error, robustness and stability under impulsive environment in addition to modelling accuracy for stationary as well as non-stationary environment besides reducing computational complexity many folds

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.Comment: 25 pages, 16 figures, submitted to IEEE Communications Surveys and Tutorials (revised Jan. 2018

Rate Optimal design of a Wireless Backhaul Network using TV White Space

The penetration of wireless broadband services in remote areas has primarily been limited due to the lack of economic incentives that service providers encounter in sparsely populated areas. Besides, wireless backhaul links like satellite and microwave are either expensive or require strict line of sight communication making them unattractive. TV white space channels with their desirable radio propagation characteristics can provide an excellent alternative for engineering backhaul networks in areas that lack abundant infrastructure. Specifically, TV white space channels can provide "free wireless backhaul pipes" to transport aggregated traffic from broadband sources to fiber access points. In this paper, we investigate the feasibility of multi-hop wireless backhaul in the available white space channels by using noncontiguous Orthogonal Frequency Division Multiple Access (NC-OFDMA) transmissions between fixed backhaul towers. Specifically, we consider joint power control, scheduling and routing strategies to maximize the minimum rate across broadband towers in the network. Depending on the population density and traffic demands of the location under consideration, we discuss the suitable choice of cell size for the backhaul network. Using the example of available TV white space channels in Wichita, Kansas (a small city located in central USA), we provide illustrative numerical examples for designing such wireless backhaul network