Fast Power Allocation Research Articles

Predefined-Time Observer-Based Fast Power Allocation for Battery Energy Storage Systems

Predefined-Time Observer-Based Fast Power Allocation for Battery Energy Storage Systems

A Fast Power Allocation Strategy for Multibeam Tracking Multiple Targets in Clutter

A collocated multiple-input–multiple-output (MIMO) radar has the ability to track multiple targets in the simultaneous multibeam working mode, where the defocused beams are transmitted to illuminate the whole space, and focused beams are received to extract target information. A fast power allocation (FPA) strategy is put forward in this mode. The predicted posterior Cramer–Rao lower bound (PCRLB) in the cluttered background is slighted relaxed and utilized as the optimization metric. The optimization model is then established as minimizing the worst case of PCRLB under the constraint of total power budget. By using the monotonic decreasing and invertible properties of the relaxed PCRLB, a fast sequential relaxation-based solver is proposed for this problem solving. At each iteration, the proposed solver tests the power allocation (PA) to one target whether should be set as the minimum level. Simulation results show the effectiveness and efficiency of the proposed solver, compared with convex optimization-based methods. The results also imply that the target distance and reflectivity are two main factors that influence the PA in our strategy.

Deep Learning-Based Approach to Fast Power Allocation in SISO SWIPT Systems with a Power-Splitting Scheme

Recently, simultaneous wireless information and power transfer (SWIPT) systems, which can supply efficiently throughput and energy, have emerged as a potential research area in fifth-generation (5G) system. In this paper, we study SWIPT with multi-user, single-input single-output (SISO) system. First, we solve the transmit power optimization problem, which provides the optimal strategy for getting minimum power while satisfying sufficient signal-to-noise ratio (SINR) and harvested energy requirements to ensure receiver circuits work in SWIPT systems where receivers are equipped with a power-splitting structure. Although optimization algorithms are able to achieve relatively high performance, they often entail a significant number of iterations, which raises many issues in computation costs and time for real-time applications. Therefore, we aim at providing a deep learning-based approach, which is a promising solution to address this challenging issue. Deep learning architectures used in this paper include a type of Deep Neural Network (DNN): the Feed-Forward Neural Network (FFNN) and three types of Recurrent Neural Network (RNN): the Layer Recurrent Network (LRN), the Nonlinear AutoRegressive network with eXogenous inputs (NARX), and Long Short-Term Memory (LSTM). Through simulations, we show that the deep learning approaches can approximate a complex optimization algorithm that optimizes transmit power in SWIPT systems with much less computation time.

Fast Power Allocation for Secure Communication With Full-Duplex Radio

This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy.

Power Allocation Schemes in OFDM-Based Femtocell Networks

Orthogonal frequency division multiplex-based femtocell networks in downlink with two-tier interference are considered in this paper. In order to satisfy the quality of service of macrocell users, a capacity maximization problem with the total and the upper power constraints by allocating power to subcarriers for a user in femtocell systems becomes important. In this paper, the optimal power allocation is derived by using the Lagrangian technique. Based on the analysis of the Karush-Kuhn-Tucker conditions, subcarriers can be classified into two sets with different power allocation strategies according to the upper power constraint. One set of subcarriers is allocated with upper power, and the other set of subcarriers is processed by using the waterfilling approach. A direct linear search scheme is presented to achieve the optimal performance by finding the threshold over the channel states of all subcarriers to determine the two sets. In order to reduce the computational load, a reduced complexity scheme is designed for the optimal solution by utilizing the relationship of the allocated power in the two sets of subcarriers. Unlike the schemes in iterative manners, a fast power allocation scheme with a near-optimal performance is also developed. Simulation results reveal that the proposed schemes outperform the existing scheme. The efficacy of the proposed schemes is demonstrated in the simulations.

Fast Power Allocation Algorithm for Cognitive Radio Networks

A fast algorithm is proposed to tackle the optimal power allocation problem for orthogonal frequency division multiplexing (OFDM)-based cognitive radio networks, where the key is to replace the Newton step with O(N^3) complexity in the barrier method by a procedure with approximate linear complexity developed based on the structure of the optimization problem. Simulation results validate that our method can always work out the optimal solution, with complexity even lower than heuristic methods that can only produce suboptimal solutions.

A Fast Power Allocation Strategy for OFDM Systems

In this letter, we propose a power allocation strategy for orthogonal frequency division multiplexing (OFDM) systems. To achieve the maximum capacity, the proposed power allocation strategy iteratively tunes the amount of power allocated to subcarriers according to the related parameters in the duality gap. The proposed strategy has a fast rate to achieve the solution and a very low complexity, and it achieves almost optimum performance.