Power Adaptation Algorithm Research Articles

Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning

This paper studies a multi-objective scheduling problem in a continuous annealing operation of the steel industry, which is to simultaneously determine the production batch sizes for coils and their schedules so as to minimize the cost of setup, earliness, and tardiness. A large batch can reduce the setup costs but lead to a cost increase in earliness and tardiness. Thus, the conflict objectives of minimizing setup cost, earliness, and tardiness can be formulated separately. In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL) for effectively obtaining the Pareto solutions. In the proposed AMODE-RDL, DRL is integrated into the MODE algorithm as a controller, which can adaptively select mutation operators and parameters according to different search domains. Computational results on randomly generated instances and the practical problem instances show that DRL can effectively guide MODE to select mutation operators and parameters. The proposed algorithm can obtain better solutions compared to other powerful multi-objective evolutionary and adaptive MODE algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Setup usually leads to a decrease in production capacity and an increase in production costs. Similarly, earliness and tardiness costs are also crucial. These costs correspond to production, customer demand, and inventory, which are common objectives in production systems. However, the three objectives are generally conflicting, and it is very hard for practitioners to make appropriate decisions with manual experience. The multi-objective optimization methods can provide different scheduling for practitioners who may choose the suitable one according to current working conditions. Accordingly, the proposed model and algorithm can extend to other fields with similar characteristic problems.

Advice complexity of adaptive priority algorithms

The priority model was introduced to capture “greedy-like” algorithms. Motivated by the success of advice complexity in the area of online algorithms, the fixed priority model was extended to include advice, and a reduction-based framework was developed for proving lower bounds on the amount of advice required to achieve certain approximation ratios in this rather powerful model. To capture most of the algorithms that are considered greedy-like, the even stronger model of adaptive priority algorithms is needed. We extend the adaptive priority model to include advice. We modify the reduction-based framework from the fixed priority case to work with the more powerful adaptive priority algorithms, simplifying the proof of correctness and strengthening all previous lower bounds by a factor of two in the process.As evidence that adding advice to adaptive priority algorithms extends both adaptive priority algorithms and online algorithms with advice, we present a purely combinatorial adaptive priority algorithm with advice for Minimum Vertex Cover on triangle-free graphs of maximum degree three. Our algorithm achieves optimality and uses at most 7n/22 bits of advice. No adaptive priority algorithm without advice can achieve optimality without advice, and we prove that an online algorithm with advice needs more than 7n/22 bits of advice to reach optimality.We show connections between exact algorithms and priority algorithms with advice. The branching in branch-and-reduce algorithms can be seen as trying all possible advice strings, and all priority algorithms with advice that achieve optimality define corresponding exact algorithms, priority exact algorithms. Lower bounds on advice-based adaptive algorithms imply lower bounds on running times of exact algorithms designed in this way.

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.

Energy-efficient resource management for OFDM modulated bidirectional relaying with quality-of-service constraint

This paper addressed the energy-efficient resource management problem of an amplify-and-forward relay-assisted bidirectional relay system under a quality-of-service (QoS) constraint. The objective is to develop a holistic resource management algorithm for joint implementation of relay selection, power adaptation and bit-rate management for optimal energy efficiency (EE). A three-stage approach is proposed to solve the energy-efficient resource management problem. At stage 1 (stage of power management), a per-subcarrier energy-efficient problem is investigated, leading to a power adaptation algorithm for maximizing the system EE and ensuring the required level of the system QoS. Within the framework of the power adaptation algorithm and by exploiting spatial diversity of multiple-relay channels, distributed relay selection is investigated at stage 2 (stage of relay management). Next, the bit-rate management problem is tackled at stage 3, namely assigning bit rate to different subcarriers to maximize the system EE further. Finally, summarizing the results achieved at the three stages, a novel EE technology combined with relay selection, bit-rate management and power adaptation is developed. Simulation results validated the correctness and the efficiency of the proposed algorithm. It is shown that the proposed algorithm can significantly reduce the total transmit power of the system while ensuring the required system QoS.

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.

Self-Organizing Ultra-Dense Small Cells in Dynamic Environments: A Data-Driven Approach

This paper presents a data-driven biadaptive self-organizing network (Bi-SON) for ultra-dense small cells (UDSC), which can improve energy efficiency and reduce interference in dynamic environments, taking account of cell switching on / off , transmission power adjustment, and traffic loads simultaneously. In the first adaptation of Bi-SON, a joint traffic load and interference aware cell ranking mechanism first determines the necessary active small cells based on traffic loads, and then ranks all the active small cells based on their carried traffic load and resulting interference. Top ranked cells will transmit at the maximum power. The last ranked $K$ cells will adjust the transmission power for interference reduction in the second adaptation function of Bi-SON, while maintaining the required quality of service. According to a polynomial regression learning approach, the total system throughput of UDSC is characterized as a function of $K$ . Compared to the baseline case when all the cells transmit with the maximum power, our proposed Bi-SON framework can improve the throughput and energy efficiency of UDSC by 73% and 169%, respectively. However, the pure switching on/off approach can only improve the throughput and the energy efficiency of UDSC by 52% and 115%, respectively. As demonstrated, even with a simple power adaptation algorithm, a learning-based Bi-SON framework can improve the performance of UDSC by taking advantage of the pervasive availability of voluminous data.

Control-Data Separation Architecture for Dual-Band mmWave Networks: A New Dimension to Spectrum Management

The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this paper, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band, and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this paper includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.

Optimized Intelligent Design for Smart Systems Hybrid Beamforming and Power Adaptation Algorithms for Sensor Networks Decision-Making Approach

During last two decades, power adaptation and beamforming solutions have been proposed for multiple input multiple output (MIMO) Ad Hoc networks. Game theory based methods such as cooperative and non-cooperative joint beamforming and power control for the MIMO ad hoc systems consider the interference and overhead reduction, but have failed to achieve the tradeoff between communication overhead and power minimization. Cooperative method using game theory achieves the power minimization, but introduced the overhead. The non-cooperative solution using game theory reduced the overhead, but it takes more power and iterations for convergence. In this paper, a novel game theory based algorithms proposed to achieve the tradeoff between power control and communication overhead for multiple antennas enabled wireless ad-hoc networks operating in multiple-users interference environment. The optimized joint iterative power adaption and beamforming method designed to minimize the mutual interference at every wireless node with constant received signal to interference noise ratio (SINR) at every receiver node. First cooperative potential game theory based algorithm designed for the power and interference minimization in which users cluster and binary weight books along used to reduce the overhead. Then the non-cooperative based approach using the reinforcement learning (RL) method is proposed to reduce the number of iterations and power consumption in networks, the proposed RL procedure is fully distributed as every transmit node require only an observation of its instantaneous beamformer label which can be obtained from its receive node. The simulation results of both methods prove the efficient power adaption and beamforming for small and large networks with minimum overhead and interference compared to state-of-art methods.

Wireless Transmission Based Image Quality Analysis Using Uni-Level Haar Wavelet Transform

This article deals with picture excellence examination by different parameters utilizing uni-level Haar wavelet transmission in excess of remote channel. The quality is analyzed based on power. The goal is towards reducing absolute power assigned in favour of picture compression and communication, while power in favour of every bit is reserved at prearranged value. Two Power Algorithms were presented. The greatest iterative power control calculation and Minimum Power Adaptation Algorithm (MPAA) are proposed. Those algorithms methodology was utilized for improving the aggregate power dispensed for multimedia such as picture because of input compression and transmission focus towards a settled bit source mutilation. Simulations were performed utilizing Haar wavelet than Additive White Gaussian Ration (AWGN) channel. Different picture excellence parameters, for example, Peak Signal to Noise Ratio (PSNR), M-Normalized Cross-Correla- tion, Average Difference; Structural Content parameters, for example, Maximum Difference, Normalized Absolute Error, Elapsed Time, CPU time, demonstrate a improved presentation with MPAA, Maximum Power Adaptation Algorithms (MAPAA) instead of Conventional Power Adaptation Algorithm (CPAA).

Performance Analysis of Feed Forward Broadband Active Noise Control System using Adaptive Algorithms

These algorithms have been applied to an extensive number of problems including noise and echo cancelling, channel equalization, signal prediction, adaptive arrays as well as many others. Noise control is the field of acoustical engineering that deals with reducing unwanted sound in the environment An active noise control (ANC) system based on adaptive filter theory was developed in the 1980s; however, only with the recent introduction of powerful but less expensive digital signal processor (DSP) hardware, such as the TMS320 family, these technologies become practical. These specialized DSPs were designed for real-time numerical processing of digitized signals. These devices have enabled the less-cost implementation of powerful adaptive ANC algorithms and encouraged the widespread development of ANC systems. We are presents general background information about ANC methods. Contrasts between passive and active noise control are mentioned. Different types of noise-control algorithms are discussed: feed forward broadband, feed forward narrowband, and feedback algorithms. In this paper detailed design of a simple ANC system using aTMS320 DSP and the implementation of that design.

A Low-Complexity Energy-Efficient Power Allocation and Rate Adaptation Scheme for Relay-Assisted Systems

In this letter, an energy-efficient power and rate adaptation algorithm for amplify-and-forward (AF) relay systems is studied with imperfect channel statement information (CSI) at transmitters. In the proposed scheme, energy efficiency (EE) is maximized under the maximum transmit power constraint, the minimum system data-rate constraint and the target packet outage probability constraint. In addition, to solve the EE maximization problem, we propose a low-complexity iterative algorithm. Numerical results illustrate the effectiveness of the proposed algorithm.

Simultaneous Deterministic Control of Distant Qubits in Two Semiconductor Quantum Dots

In optimal quantum control (OQC), a target quantum state of matter is achieved by tailoring the phase and amplitude of the control Hamiltonian through femtosecond pulse-shaping techniques and powerful adaptive feedback algorithms. Motivated by recent applications of OQC in quantum information science as an approach to optimizing quantum gates in atomic and molecular systems, here we report the experimental implementation of OQC in a solid-state system consisting of distinguishable semiconductor quantum dots. We demonstrate simultaneous high-fidelity π and 2π single qubit gates in two different quantum dots using a single engineered infrared femtosecond pulse. These experiments enhance the scalability of semiconductor-based quantum hardware and lay the foundation for applications of pulse shaping to optimize quantum gates in other solid-state systems.

Energy-Efficient Power Adaptation over a Frequency-Selective Fading Channel with Delay and Power Constraints

This paper presents an energy-efficient power allocation for a multicarrier link over a frequency-selective fading channel with a delay-outage probability constraint. The power adaptation maximizes the system energy efficiency (EE), formulated as the ratio of the achieved effective capacity (EC) to the total expenditure power, including both transmission power and rate-independent circuit power. We prove that this objective function is quasi-concave in the transmission power, and derive the global optimum solution using fractional programming. Based on the obtained solution, we develop a power adaptation algorithm consisting of two steps: (i) establishing the optimum average power level corresponding to the maximum achievable EE with no transmit power constraint, and then (ii) for a given power constraint, jointly distributing the power over time and frequency based on the constraint and the optimum power level found in the first step. Analytical results show that the proposed EE-based power allocation has a structure similar to the allocation that maximizes the EC, but with a different cut-off threshold. Our proposed joint EE-optimal power allocation provides significant EE gains over both the joint spectral-efficient and independent-subcarrier EE-based power allocation schemes, where the rate-energy tradeoff becomes more pronounced with higher frequency selectivity.

QoS-Aware and Energy-Efficient Resource Management in OFDMA Femtocells

We consider the joint resource allocation and admission control problem for Orthogonal Frequency-Division Multiple Access (OFDMA)-based femtocell networks. We assume that Macrocell User Equipments (MUEs) can establish connections with Femtocell Base Stations (FBSs) to mitigate the excessive cross-tier interference and achieve better throughput. A cross-layer design model is considered where multiband opportunistic scheduling at the Medium Access Control (MAC) layer and admission control at the network layer working at different time-scales are assumed. We assume that both MUEs and Femtocell User Equipments (FUEs) have minimum average rate constraints, which depend on their geographical locations and their application requirements. In addition, blocking probability constraints are imposed on each FUE so that the connections from MUEs only result in controllable performance degradation for FUEs. We present an optimal design for the admission control problem by using the theory of Semi-Markov Decision Process (SMDP). Moreover, we devise a novel distributed femtocell power adaptation algorithm, which converges to the Nash equilibrium of a corresponding power adaptation game. This power adaptation algorithm reduces energy consumption for femtocells while still maintaining individual cell throughput by adapting the FBS power to the traffic load in the network. Finally, numerical results are presented to demonstrate the desirable operation of the optimal admission control solution, the significant performance gain of the proposed hybrid access strategy with respect to the closed access counterpart, and the great power saving gain achieved by the proposed power adaptation algorithm.

QoS-Guaranteed Power Control Mechanism Based on the Frame Utilization for Femtocells

The paper focuses on a power control mechanism and proposes a novel approach for dynamic adaptation of femtocells' transmitting power. The basic idea is to adapt the transmitting power of femtocells according to current traffic load and signal quality between user equipments and the femtocell in order to fully utilize radio resources allocated to the femtocell. The advantage of the proposed scheme is in provisioning of high quality of service level to the femtocell users, while interference to users attached to macrobase station is minimized. The paper proposes the power adaptation algorithm and evaluates its performance in terms of mobility events, achieved throughput, and FAPs transmitting power. Performed simulations show that the proposed scheme can significantly reduce the number of mobility events caused by passerby users and thus to minimize signaling overhead generated in the network. In addition, our proposal enhances overall throughput for most of the investigated scenarios in comparison to other power control schemes.

Joint Rate and Power Adaptation for Wireless Local Area Networks in Generalized Nakagami Fading Channels

In this paper, a channel-driven rate and power adaptation (CDRPA) algorithm is proposed for wireless local area networks (WLANs) in generalized Nakagami fading channels. Through the channel information of the first packet, the CDRPA algorithm can lower the complexity of comparing various rates and power levels. In the case of the IEEE 802.11 WLAN, the computation complexity of the CDRPA algorithms is reduced by more than 94% compared with the complete-search link adaptation. A physical and/or medium access control (MAC) cross-layer analytical model is also developed to evaluate the goodput and energy efficiency of the carrier sense multiple access with a collision avoidance MAC protocol in Nakagami fading channels. Our numerical results show that the CDRPA algorithm is not only more energy efficient in the Nakagami fading channel but can also achieve comparable and even higher throughput compared with the complete-search link adaptation approach.

Quality of service constrained throughput optimisation for joint adaptive transmission with automatic repeat request over block-fading channels

An optimisation framework for wireless link adaptation which maximises the system throughput subject to a packet-level quality of service (QoS) constraint is presented. The authors consider joint adaptive variation of the transmitted power level, transmission data rate and packet-error-rate (PER) at the physical layer to improve the throughput performance of the selective-repeat automatic repeat request (SR-ARQ) protocol over block-fading channels. Specifically, for discrete-rate coded M-QAM schemes, the authors present a power and rate adaptation algorithm that guarantees a target PER constraint. The proposed framework also facilitates optimising the throughput performance for delay constrained wireless applications, which imposes a limit on the number of retransmissions for the ARQ. In particular, a link adaptation scheme is presented, which guarantees a target packet-loss rate (PLR) when a truncated SR ARQ protocol at the data-link layer is employed. Numerical results indicate that the proposed adaptation schemes compared with other adaptive schemes noticeably enhance the system throughput. Also, in the case of truncated ARQ, the results illustrate a fundamental trade-off between the delay and PLR QoS metrics achieved at the maximum throughput performance.

Stochastic Learning Solution for Distributed Discrete Power Control Game in Wireless Data Networks

Distributed power control is an important issue in wireless networks. Recently, noncooperative game theory has been applied to investigate interesting solutions to this problem. The majority of these studies assumes that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and Qualcomm's proposal to the IS-95 standard use a finite number of discretized power levels. This motivates the need to investigate solutions for distributed discrete power control which is the primary objective of this paper. We first note that, by simply discretizing, the previously proposed continuous power adaptation techniques will not suffice. This is because a simple discretization does not guarantee convergence and uniqueness. We propose two probabilistic power adaptation algorithms and analyze their theoretical properties along with the numerical behavior. The distributed discrete power control problem is formulated as an N-person, nonzero sum game. In this game, each user evaluates a power strategy by computing a utility value. This evaluation is performed using a stochastic iterative procedures. We approximate the discrete power control iterations by an equivalent ordinary differential equation to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium or even only mixed equilibrium may exist are also studied. Experimental results are presented for several cases and compared with the continuous power level adaptation solutions.

Distributed topology control of wireless networks

We propose and analyaze a distributed control law that will maintain prescribed local properties of a wireless ad hoc network in the presence of node mobility, MAC layer power control and link fades. The control law uses a simple and intuitive power adaptation mechanism. We consider as an example the topology requirement of maintaining the out-degrees of each node at prescribed values and keeping the in-degree close to the out-degree. The topology objective is achieved by adapting the transmission power based only on local information. This power adaptation algorithm is analyzed using the o.d.e. approach to stochastic approximation. Simulation results verify the analysis and demonstrate its effectiveness. We also study the ability of the proposed objective to maintain connectivity. Although many heuristics are described in the literature to maintain local topological properties, the algorithm proposed in this paper is the first one that has proven convergence properties.

Constant-power waterfilling: performance bound and low-complexity implementation

In this letter, we investigate the performance of constant-power waterfilling algorithms for the intersymbol interference channel and for the independent identically distributed fading channel where a constant power level is used across a properly chosen subset of subchannels. A rigorous performance analysis that upper bounds the maximum difference between the achievable rate under constant-power waterfilling and that under true waterfilling is given. In particular, it is shown that for the Rayleigh fading channel, the spectral efficiency loss due to constant-power waterfilling is at most 0.266 b/s/Hz. Furthermore, the performance bound allows a very-low-complexity, logarithm-free, power-adaptation algorithm to be developed. Theoretical worst-case analysis and simulation show that the approximate waterfilling scheme is very close to the optimum.