Coordinated scheduling of thermostatically controlled real-time systems under peak power constraint

We consider power adaptation strategies for binary phase-shift keying signals in Rayleigh fading channels under the assumption that channel state information is provided at both the transmitter and the receiver. We first derive a closed-form expression for the optimal power adaptation that minimizes average bit-error rate (BER) subject to average and peak transmission power constraints. Then, we analyze the average BER for channel inversion power adaptation with the same constraints. Our results show that the performance difference between the optimal power adaptation and the channel inversion becomes negligibly small as available average transmission power increases and/or peak-to-average power ratio decreases. We also find that an optimal peak-to-average power ratio exists that minimizes the average BER in the channel inversion scheme.

This paper considers a two user Gaussian multiple-input single-output (MISO) broadcast channel with a per-antenna peak power constraint (or simply peak power constraint). It is more realistic to consider the peak power constraint on each transmit antenna because each antenna is equipped with its own power amplifier in many practical implementations. Assuming the perfect channel state information (CSI) at the transmitter, we propose an achievable scheme using a dirty-tape coding (DTC). The uniform input in a fixed range of the DTC scheme helps to control the peak power of the transmit signal easily. We also present an optimization algorithm that finds the capacity achieving beamforming vectors and power allocations under a per-antenna average power constraint used in our achievable scheme. Simulation results show that as the transmit power increases, the achievable rate region under the peak power constraint is getting close to the capacity region under the reduced per-antenna average power constraint by 1/3. Compared to a non-DTC scheme based on minimum mean square error (MMSE) beamforming, the proposed scheme performs better.

The difficulty of modeling energy consumption in communication systems leads to challenges in energy harvesting (EH) systems, in which nodes scavenge energy from their environment. An EH receiver must harvest enough energy for demodulating and decoding. The energy required depends upon factors, such as code rate and signal-to-noise ratio, which can be adjusted dynamically. We consider a receiver which harvests energy from the transmitter and other ambient sources, meaning the received signal is used for both EH and information decoding. Assuming a generalized function for energy consumption, we maximize the total number of information bits decoded, under both average and peak power constraints at the transmitter, by carefully optimizing the power used for EH, power used for information transmission, fraction of time for EH, and code rate. For transmission over a single block, we find there exist problem parameters for which either maximizing power for information transmission or maximizing power for EH is optimal. In the general case, the optimal solution is a tradeoff of the two. For transmission over multiple blocks, we give an upper bound on performance and give sufficient and necessary conditions to achieve this bound. Finally, we give some numerical results to illustrate our results and analysis.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper considers the performance of time-domain power-allocation schemes with average and peak power constraints and proposes a constant-power allocation (CPA) scheme. In a Nakagami-<formula formulatype="inline"><tex Notation="TeX"> $m$</tex></formula> fading environment, an exact closed-form expression for the ergodic capacity of the proposed CPA scheme is derived, and the effect of the fading parameter <formula formulatype="inline"><tex Notation="TeX">$m$</tex></formula> on the performance degradation due to a peak power constraint is investigated. The proposed scheme shows a near-optimum performance within 5% of the optimal capacity and provides a noticeable performance improvement as it reduces the peak-to-average power ratio (PAPR) with a signal-to-noise ratio (SNR) gain of approximately 8.5 dB while also mitigating the intercell interference in the uplink. </para>

In this paper, training-based transmissions over a priori unknown Rayleigh block fading channels are considered. The input signals are assumed to be subject to peak power constraints. Prior to data transmission, channel fading coefficients are estimated in the training phase with the aid of pilot symbols. In this setting, the capacity and capacity-achieving input distribution are studied. The magnitude distribution of the optimal input is shown to be discrete with a finite number of mass points. The capacity, bit energy requirements, and optimal resource allocation strategies are obtained through numerical analysis. The bit energy is shown to grow without bound as SNR decreases to zero due to the presence of peakedness constraints. Capacity and energy-per-bit are also analyzed under the assumptions that the transmitter interleaves the data symbols before transmission over the channel, and per-symbol peak power constraints are imposed. Comparisons of the performances of training-based and noncoherent transmission schemes are provided.

Co-existence of radar and communication systems based on cooperative spectrum sharing is of significant interest in recent years. This letter proposes two joint power allocation schemes for radar and communication spectrum sharing, where both systems use the same bandwidth with cross interference between them. The proposed joint design maximizes a performance metric of one system while meeting a service constraint requirement for the other, i.e., throughput for the communication system or signal-to-interference-plus-noise ratio (SINR) for the radar. For both designs, we show that either the radar or the communication system needs to transmit at peak power to achieve the optimum solution, depending on the relation among the peak power constraint, channel conditions, and service constraints.

In this article, we adopt outage probability (λ-capacity) in fast-fading channels as a pay-off function in a zero-sum game between a legitimate transceiver pair and an uncorrelated Gaussian jammer. The transmitter aims at minimising the outage probability, while the jammer attempts to maximise the outage probability. We consider both peak (over each codeword) and average (over all codewords) power constraints. For peak power constraints, a transmission rate is either supported by the system, or if too large, causes the whole transmission to fail. By imposing average power constraints, large rates can be supported at the cost of positive probability of codeword error. Maxmin (where the jammer is assumed to play first) and minimax (where the transmitter is assumed to play first) power control strategies are developed under each power constraint, which show that no Nash equilibrium of pure strategies exists under the average power constraint when the follower has perfect knowledge about the randomisation outcome of the first player.

Development of energy efficient design proposals for air conditioned mosques: Temperate humid climate case

In this paper, a joint power and subcarrier allocation scheme in single-cell uplink OFDMA systems is proposed based on game theory. Our goal is to maximize the power efficiency of each user under peak power constraint. For the purpose, a utility function is designed which can enhance the rate per joule of energy consumed greatly, and the existence and uniqueness of the Nash equilibrium of the function is proved. To bring Pareto improvement, and to overcome the far-near effect, the pricing scheme is introduced. The simulation results show that the power efficiency of the proposed algorithm increases greatly over that of the MaxRt+WF (maximal marginal rate subcarrier and waterfilling power allocation) and MaxFA+WF (fixed subcarrier allocation and waterfilling power allocation).

This paper considers a max-min fairness power control problem for the spectral efficiency of multiuser massive multiple-input multiple-output systems in a uplink transmission, where a base station receives data signals from the various users. A physical channel model in which the angular domain is separated into a finite number of distinct directions is also considered in this paper. Based on the large-scale fading coefficients, a power control method is formulated as an optimization problem in order to maximize the minimum spectral efficiency among the various users under the peak power constraint. This optimization problem is solved by employing a geometric program. The numerical results are conducted by two practical scenarios to evaluate the proposed power control method. In both scenarios, it has been shown that the proposed power control method is superior to other existing schemes in terms of minimum spectral efficiency.

This brief investigates the spectral efficiency of multiuser massive multiple-input multiple-output systems with a very large number of antennas at a base station serving single-antenna users. A practical physical channel model is proposed by dividing the angular domain into a finite number of distinct directions. A lower bound on the achievable rate of uplink data transmission is derived using a linear detector for each user and employed in defining the spectral efficiency. The obtained lower bound is further modified for the maximum-ratio combining and zero-forcing receivers. A power control scheme based on the large-scale fading is also proposed to maximize the spectral efficiency under peak power constraint. Experiments are conducted to evaluate the obtained lower bounds and the performance of the proposed method. The numerical results show a comparison between the performances of both receivers along with the advantage of the proposed power control scheme in terms of the spectral efficiency.

As a promising technology, reconfigurable intelligent surface (RIS) can customize the wireless channel through the adjustment of its phase, and strengthen the localization performance and anti-jamming capability of multiple input multiple output (MIMO) radar. Thus, it is imperative to investigate a jamming strategy against the RIS-aided MIMO radar localization system. In this paper, we consider a jamming power allocation problem for RIS-aided MIMO radar based on cramér-rao bound (CRB) criteria. To this end, we first derive the CRB regarding target location estimation of RIS-aided MIMO radar in the presence of jammers. Subsequently, we formulate the jamming power allocation problem to maximize the minimum of the average CRB of RIS-aided MIMO radar via jointly optimizing the jamming power and RIS phase under the power budget and peak power constraints. Due to the variable coupling and high non-convexity of CRB, the optimization problem is extremely challenging to tackle. To solve the non-convex problem, we develop an alternating iterative algorithm with respect to the jamming power allocation and RIS phase design optimization subproblems. Specifically, we first transform the jamming power allocation subproblem into the form of linear programming for solution. Then, the RIS phase design subproblem is relaxed by the arithmetic-harmonic mean inequality to circumvent the complex matrix inversion, and converted into a fractional programming problem that can be solved through quadratic transformation approach. Owing to a degeneration of the solution induced by the inequality relaxation, we further propose a tightening-relaxation strategy to improve the solution quality, and re-optimize the RIS phase based on the previously derived solution. The ultimate solution can be achieved by alternately optimizing the jamming power and the re-optimized RIS phase. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Over the past decade, the compute resource requirements of applications has continued to grow. This leads to an increase in the required infrastructure that poses additional challenges to mitigating energy consumption and peak power draws. Apache Mesos has been a dominant player in the cluster management space, acting as a middleware between frameworks and the underlying infrastructure. Due to its high availability, fault-tolerance, and scalability, Mesos is used in the industry to run massive scale workloads. Datacenters typically handle continuous dynamically varying workloads, potentially leading to high peak power and energy consumption, in-turn leading to high cost of operation. Given a set of scheduling policies that have been proven to be beneficial in lowering peak power and/or energy consumption, we propose a mechanism of switching between these policies in order to adapt to the dynamic variation in the workloads and the changes in the state of the cluster. Our experiments show that adapting to the variation in the workload can lead to a 9.2% reduction in max peak power consumption, 6.9% in total energy consumption, and a 6.4% reduction in makespan when compared to using a single scheduling policy.

This study addresses the pressing issue of energy consumption and efficiency in the Kingdom of Saudi Arabia (KSA), a region experiencing growing demand for energy resources. Temperature control plays a vital role in achieving energy efficiency; however, traditional control systems may struggle to adapt to the non-linear and time-varying characteristics of the problem. To tackle this challenge, a fuzzy petri net (FPN) controller is proposed as a more suitable solution that combines fuzzy logic (FL) and petri nets (PN) to model and simulate complex systems. The main objective of this research is to develop an intelligent energy-saving framework that integrates advanced methodologies and air conditioning (AC) systems to optimize energy utilization and create a comfortable indoor environment. The proposed system incorporates user identification to authorize individuals who can set the temperature, and FL combined with PN is utilized to monitor and transmit their preferred temperature settings to a PID controller for adjustment. The experimental findings demonstrate the effectiveness of integrating the FPN controller with a convertible frequency AC compressor in significantly reducing energy consumption by 94% compared to using the PN controller alone. The utilization of the PN controller alone resulted in a 25% reduction in energy consumption. Conversely, employing a fixed-frequency compressor led to a 40% increase in energy consumption. These results emphasize the advantages of integrating FL into the PN model, as it effectively reduces energy consumption by half, highlighting the potential of the proposed approach for enhancing energy efficiency in AC systems.

The Internet of Things (IoT) connects electrical appliances that enable data transfer for communication without human intervention. IoT has evolved, and its implementation has been extended to residential areas. It can be said that all residents use fans as cooling appliances. In Malaysia, having a fan is not sufficient due to its hot temperature throughout the year. Therefore, most of the residents use air conditioners as an additional cooling appliance. Using air conditioners regularly could contribute to high energy consumption. Furthermore, excessive energy consumption occurs when an occupant of a residential building forgets to switch off electrical appliances such as fans and air conditioners. In addition, leaving electrical appliances turning on when nobody is at home just wastes energy. This work aims to develop an IoT-based smart home controlling system for minimizing energy consumption. This system enables automatic control that depends on room temperature and motion detection. Various types of sensors, such as temperature sensor, humidity sensor, and motion sensor, are used to switch on/off the air conditioner and fan. The air conditioner and fan will be alternately switched on and off depending on the ideal room temperature. The testing results show a significant reduction in energy consumption and a promising decrease in the electricity bill. Future works should be focusing on determining the over limit energy consumption. On top of that, this research would be best to try on simulation to get better results.