Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.     

Power control of optical CDMA star networks

In this paper, we apply the power control concept to optical CDMA star networks. Two approaches are considered, namely, centralized and distributed power control. Both approaches are used to optimize the optical transmit power and to maximize network capacity in terms of the number of users satisfying a target signal to interference (SIR) ratio. Centralized algorithms result in the optimum power vector while distributed algorithms are more suitable for practical system implementation and eliminate the need for a centralized control node. Both analytical and simulation results show significant improvement in the performance of the power controlled optical CDMA system. For instance, in a network of 31 nodes, a doubling of the capacity as compared to the non power control case is obtained. Furthermore, we show in the interference-limited case that the network performance is upper bounded by the number of nodes and the correlation properties of the employed code rather than network attenuation and optical fiber lengths. The concept of network partitioning is then introduced to simplify optimum power calculations. Using network partitioning, we find in the interference-limited case that the optical fibers after the star coupler are irrelevant to the optimum power evaluation.     

次级声源优化布放的局部空间有源降噪

针对区域有源降噪问题,为获得更优降噪效果,根据实际次级通路传递函数,提出次级声源优化布放的有源控制系统并详细比较了两种次级声源优化布放算法与次级声源均匀布放的实际降噪效果。应用的第一种次级声源优化算法是l₂范数约束的约束匹配追踪算法,第二种次级声源优化算法是l₁范数约束的稀疏正则化方法。在全消声室中利用扬声器线阵进行多通道有源降噪实验研究,实验结果表明,在200～1000 Hz,次级声源优化布放的控制系统的平均降噪量比次级声源均匀布放的控制系统的平均降噪量多5 dB左右;在1100～1900 Hz,次级声源优化布放的控制系统的平均降噪量比次级声源均匀布放的控制系统的平均降噪量多11～13 dB左右,次级声源优化布放的控制系统的降噪量分布更加均匀且次级声源输出能量更小。此外,两种优化算法中,稀疏正则化方法的降噪效果更佳。     

GA-based optimization of a MIMO ANC system considering coupling of secondary sources in a telephone kiosk

In this paper a multi-input, multi-output (MIMO) active noise control system with the aim of global reduction of broadband noise in a telephone kiosk is addressed. The model selected for this optimization problem is the acoustic environment of an enclosure taking into account the effect of coupling of secondary sources used for control purpose. This optimization involves finding the best locations for loudspeakers and microphones inside the enclosure as well as optimizing the control signals considering secondary source coupling.Previous results show that in order to be able to reduce acoustic noise globally inside the enclosure, the frequency range of 50-300 Hz must be selected for control purpose. The mean of acoustic potential energy of the enclosure, when excited in this frequency range, is adopted as a performance measure. This performance index is penalized with the power of the signal required to excite secondary loudspeakers, in order to avoid placements that may need high voltage power amplifier for a desired performance. To find the solution of this problem, i.e. the global minimum of the performance index, several genetic algorithms are proposed and compared. In order to attain the best achievable performance in reaching the global minimum, the parameters of these genetic algorithms are tuned, and used for optimization purpose. Numerical simulations of the acoustical potential energy as well as the sound pressure at different heights of the kiosk, when active noise control (ANC) system operates, confirm the optimality of the locations proposed by the genetic algorithm.     

Transmitted power allocation/control for multi-band MC-CDMA

Transmit power allocation over multiple subcarriers is an effective way to improve system error probability performance and to save power in traditional multi-carrier systems. In this paper, we derive the optimum power allocation algorithm for multi-band MC-CDMA systems, where optimum is defined as minimal BER under the constraint of fixed transmitted power, assuming knowledge of power attenuation of different sub-bands. With a two-band MC-CDMA system and Rayleigh channel fading, we show that a BER performance improvement based on the optimum allocation can only be attained over a limited range of transmit power and a limited range of sub-band power attenuation difference values. This performance improvement is also modest compared with a uniform power allocation, which suggests that the uniform power allocation is near optimal for transmit power control under our assumptions. Two simple transmitted power control algorithms are provided, and the controlled transmit power for a two-band system is shown to be a linear function of the power attenuation difference between the two bands for a large range of these attenuation differences. The small non-linear range implies that in using a multi-band channel, any savings in total transmitted power can occur only when the power attenuation difference between the two bands is small.     

Distributed power allocation for spectrum sharing in mutually interfering wireless systems

Allocation of transmit power is critical for spectrum sharing and coexistence of mutually interfering wireless systems. In this paper we present a novel approach for allocation of transmit power, which is based on a non-greedy procedure that aims at maximizing transmission rate while also controlling interference levels. The proposed approach is fully distributed and requires no central control or coordination. Numerical results obtained from simulations are presented to illustrate the performance of the proposed approach in both sparse and dense environments. In sparse wireless environments, where there are fewer mutually interfering wireless links than available frequency bands, the proposed approach yields power allocations which outperform those obtained by applying alternative power allocation strategies, while in dense environments, where there are more interfering links than available frequency bands, the proposed approach yields power allocations with performance similar to those of existing power strategies. Thus, the distributed power allocation procedure based on the proposed approach is a drop-in replacement algorithm that yields better system throughput than existing algorithms for spectrum sharing.     

Simulation of a Helicon plasma source using a multivariable fuzzymodel

This paper reports work on the development of an automatic control system for a Helicon plasma processing source. The lack of a definitive physical model for the plasma physics of the source and the power coupling mechanism to the plasma precludes the use of traditional control algorithms. This paper develops a fuzzy model that simulates the behavior of the plasma source using the process of genetic algorithms to identify and optimize the parameters of the fuzzy model. This type of model will eventually be used to test a fuzzy control system for the plasma source. In this work, an extensive set of experimental data was acquired where the magnetic field and input power to the plasma source were varied over a wide range while the electron number density was measured. From this learning dataset, the genetic algorithm derived the values of the parameters for the difference equation that describes the system. The fuzzy model so constructed was used to predict the behavior of the source from known input parameters. Comparing the predictions with experimental observations showed that the fuzzy model was generally able to predict the behavior of the plasma as its input parameters were varied with a precision of better than 10%     

Game theory and power control in ultrawideband networks

This paper describes a theoretical framework for the design and analysis of power control algorithms for high-throughput wireless networks using ultrawideband (UWB) technologies. The tools of game theory are shown to be expedient for deriving scalable, energy-efficient, distributed power control schemes to be applied to a population of battery-operated user terminals in a rich multipath environment. In particular, the power control issue is modeled as a dynamic noncooperative game in which each user chooses its transmit power so as to maximize its own utility, which is defined as the ratio of throughput to transmit power. Although distributed (noncooperative) control is known to be suboptimal with respect to the optimal centralized (cooperative) solution, it is shown via large-system analysis that the game-theoretic distributed algorithm based on Nash equilibrium exhibits negligible performance degradation with respect to the centralized socially optimal configuration. The framework described here is general enough to also encompass the analysis of code division multiple access (CDMA) systems and to show that UWB slightly outperforms CDMA in terms of achieved utility at the Nash equilibrium.     

一种改进的稀疏近似最小方差DOA估计算法研究

针对稀疏信号的超分辨方位估计问题,提出一种可变因子的稀疏近似最小方差算法(α-Sparse Asymptotic Minimum Variance,简记为SAMV-α)。该算法利用一个折衷参数进行最大似然估计值和稀疏性能的折衷处理,在迭代过程中改变稀疏近似最小方差算法(Sparse Asymptotic Minimum Variance,SAMV)的指数因子,得到强稀疏性能和超低旁瓣的方位谱图,实现邻近目标的超分辨方位估计和相干处理性能,且无需预估角度和信源数目等先验信息,并且折衷参数的取值为0到1之间,取值区间明确,避免了稀疏信号处理算法中正则因子选取困难的弊端。计算机仿真表明SAMV-α算法方位估计性能明显优于波束扫描类算法和子空间类算法,与同类型稀疏信号处理类算法相比仍具有较高的方位估计精度,同时对于邻近声源分辨能力,SAMV-α算法较SAMV-1算法性能提高约3dB。海上试验数据处理给出了分辨率更高的方位时间历程(Bering-Time Recording,BTR)图,有效验证了SAMV-α算法的性能。      

Piezoelectric transducer design via multiobjective optimization

The design of piezoelectric transducers is usually based on single-objective optimization only. In most practical applications of piezoelectric transducers, however, there exist multiple design objectives that often are contradictory to each other by their very nature. It is impossible to find a solution at which each objective function gets its optimal value simultaneously. Our design approach is to first find a set of Pareto-optimal solutions, which can be considered to be best compromises among multiple design objectives. Among these Pareto-optimal solutions, the designer can then select the one solution which he considers to be the best one. In this paper we investigate the optimal design of a Langevin transducer. The design problem is formulated mathematically as a constrained multiobjective optimization problem. The maximum vibration amplitude and the minimum electrical input power are considered as optimization objectives. Design variables involve continuous variables (dimensions of the transducer) and discrete variables (the number of piezoelectric rings and material types). In order to formulate the optimization problem, the behavior of piezoelectric transducers is modeled using the transfer matrix method based on analytical models. Multiobjective evolutionary algorithms are applied in the optimization process and a set of Pareto-optimal designs is calculated. The optimized results are analyzed and the preferred design is determined.     

Four-Objective Optimizations for an Improved Irreversible Closed Modified Simple Brayton Cycle

An improved irreversible closed modified simple Brayton cycle model with one isothermal heating process is established in this paper by using finite time thermodynamics. The heat reservoirs are variable-temperature ones. The irreversible losses in the compressor, turbine, and heat exchangers are considered. Firstly, the cycle performance is optimized by taking four performance indicators, including the dimensionless power output, thermal efficiency, dimensionless power density, and dimensionless ecological function, as the optimization objectives. The impacts of the irreversible losses on the optimization results are analyzed. The results indicate that four objective functions increase as the compressor and turbine efficiencies increase. The influences of the latter efficiency on the cycle performances are more significant than those of the former efficiency. Then, the NSGA-II algorithm is applied for multi-objective optimization, and three different decision methods are used to select the optimal solution from the Pareto frontier. The results show that the dimensionless power density and dimensionless ecological function compromise dimensionless power output and thermal efficiency. The corresponding deviation index of the Shannon Entropy method is equal to the corresponding deviation index of the maximum ecological function.     

Optimization of combined endoreversible Carnot heat engines with different objectives

Taking the output power,thermal efficiency,and thermo-economic performance as the optimization objectives,we optimize the operation parameters of a thermodynamic system with combined endoreversible Carnot heat engines in this paper.The applicabilities of the entropy generation minimization and entransy theory to the optimizations are discussed.For the discussed cases,only the entransy loss coefficient is always agreeable to the optimization of thermal efficiency.The applicabilities of the other discussed concepts to the optimizations are conditional.Different concepts and principles are needed for different optimization objectives,and the optimization principles have their application preconditions.When the preconditions are not satisfied,the principles may be not applicable.     

80 MVA脉冲机组超同步调速装置控制技术仿真

介绍了中国环流器二号A(HL-2A)装置80 MVA脉冲发电机组超同步调速装置的基本组成,其模拟控制系统需要数字化改造。基于异步电动机转子交交变频电路,以矢量控制算法为核心,结合电压前馈和断续补偿等理论,对超同步调速装置的控制技术进行了理论分析,并建立了PSIM仿真模型,仿真结果显示,该控制算法能很好实现对机组转速、拖动电机转子电流频率和幅值的有效控制,实现机组超同步调速,为机组超同步调速装置控制系统的数字化改造提供了重要参考。     

Noninvasive system identification for multichannel broadband active noise control

Many real-world applications of active noise control are characterized by transfer functions that vary significantly and unpredictably. The controller's transfer-function models must adapt to these variations. Presented here is a class of adaptive filters that accomplish quasiperiodic system identification updates for feedforward control by using blocks of input-output histories. The algorithms form a one-dimensional family linking normalized least-mean squares (LMS) adaptive filters and block recursive least-squares, termed "block projection" algorithms, and generalize the noninvasive system identification studied by Sommerfeldt and Tichy. The system identification proceeds noninvasively, producing nonparametric impulse responses. Simulations show that the algorithm's convergence is faster than that of normalized LMS, even after the additional overhead of computing the update is taken into account. Both the multichannel generalization and application of these algorithms to system identification are novel. Simulations of the algorithms' performance using measured data are presented here, while experimental results of an implemented algorithm are contained in the companion paper.     

IRS-assisted covert communication with eavesdropper’s channel and noise information uncertainties

This paper considers an intelligent reflecting surface (IRS)-assisted covert communication system, where an IRS is deployed for the covert transmission from a legitimate transmitter (Alice) to a legitimate receiver (Bob) with the presence of an eavesdropper (Willie). Since Willie is not a legitimate communication node and cannot control the IRS’s reflection, it has uncertainty about the channel information of the Alice-IRS and IRS-Willie links. Meanwhile, Willie also has uncertainty about its noise power. Having such channel and noise information uncertainties, Willie finds an optimal power detection threshold to minimize its false detection probability. Under this system setup, we investigate maximizing the covert rate of the legitimate communication from Alice to Bob, by jointly optimizing Alice’s transmit power and the IRS’s reflecting phase shifts. We have proposed two efficient joint optimization algorithms for the continuous and discrete IRS reflecting phase shift cases, respectively. The proposed algorithm for the previous case is based on the semidefinite relaxation technique, and that for the latter case is based on the alternating optimization technique. Simulation results have demonstrated the superiority and necessity of jointly optimizing the transmit power and IRS reflecting phase shifts on improving the covert rate performance, as compared to other benchmark schemes.     

Design of pre-processing algorithms for efficient MIMO–OFDM receiver architectures

Modern wireless communication applications are characterized by the need for advanced signal processing techniques such as Multiple-Input Multiple-Output (MIMO) technology for achieving high throughput and diversity and Orthogonal Frequency Division Multiplexing (OFDM) for achieving robustness to multipath fading. The implementation of such techniques at the transceiver level typically involves the design of algorithms with high processing complexity.This paper considers the efficient design of MIMO–OFDM receivers in preamble-based systems and addresses the problem of large processing delays associated with pre-computations and symbol detection. The existence of large processing delays has a huge impact on the performance and resource requirements (vector processing, increased clock rates and increased power consumption) of modern receivers. More specifically, we address the performance and complexity bottleneck introduced by the pre-computations involved for MIMO–OFDM channel decomposition. We propose a redesign of channel decomposition algorithms which achieves a better matching of the processing rate of MIMO–OFDM receivers to the real-time processing deadlines imposed by the structure of the incoming data packets. It is demonstrated that for a specific MIMO-OFDM channel training frame structure (alternating antenna preamble), simple modifications to typical channel decomposition algorithms can achieve significant processing performance and complexity gains compared to typical receiver designs.     

An automatic step-size adjustment algorithm for LMS adaptive filters,and an application to channel estimation

We propose a least-mean-square adaptive filter with automatic step-size adjustment (ASSA). At each time instant when a new observation of the input signal arrives, a new step-size parameter is chosen such that the sum of the squares of the measured estimation errors up to that current time instant is minimized. This step size, after being normalized by the power of the current tapped filter input, is used to update the filter weights for the next time instant. The filter weights are thus updated automatically without the aid of any preset control parameters. When applied to channel estimation, simulation results show the performance advantage of the ASSA algorithm over the existing step-size adjustment algorithms under different wireless channel environments.     

基于Pareto优化理论的多目标超椭梯度线圈设计

磁共振系统梯度线圈设计是一个多目标优化问题,在设计时需要综合考虑能耗、磁场能、线性度等设计要求.这些设计要求通常难以同时获得极小解,因此在设计梯度线圈时需要权衡线圈的各方面的设计需求.本文基于柱面可展性和流函数设计方法,结合Pareto优化方法实现了在超椭圆柱设计表面上梯度线圈的多目标设计.分别分析了磁场能、能耗目标对梯度线圈线性度、线圈构型的影响;并在Pareto解空间中分析各目标的相互变化关系,通过数值算例验证了该方法在超椭梯度线圈设计时的有效性与灵活性.优化结果显示,在满足线性度误差小于5%,能耗与磁场能分别小于用户设定值的设计约束下,梯度线圈的多目标设计存在多个局部优化解.该方法可以直观地比较相同目标函数值的情况下各单目标的具体表现,有利于实现不同的设计要求下梯度线圈的最终定型设计.     

Active noise control in a free field with virtual sensors

The zone of local control around a "virtual energy density sensor" is compared with that offered by an actual energy density sensor, a single microphone, and a virtual microphone. Intended as an introduction to the concept of forward difference prediction and a precursor to evaluating the virtual sensor control algorithms in damped enclosures, this paper investigates an idealized scenario of a single primary sound source in a free-field environment. An analytical model is used to predict the performance of the virtual error sensors and compare their control performance with their physical counterparts. The model is then experimentally validated. The model shows that in general the virtual energy density sensor outperforms the actual energy density sensor, the actual microphone, and the virtual microphone in terms of centering a practically sized zone of local control around an observer who is remotely located from any physical sensors. However, in practice, the virtual sensor algorithms are shown to be sensitive (by varying degrees) to short wavelength spatial pressure variations of the primary and secondary sound fields.