Traditional Water-filling Algorithm Research Articles

Waveform design through the trade-off relationship between the MI criterion and the SINR criterion

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks.

Power Optimization of UOWC-MIMO-OFDM System Based on PSO-WF Algorithm

Under the constraint that the total transmission power is constant, when multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) technology is applied to the signal transmission design in the underwater optical wireless communication system (UOWC), the traditional water-filling algorithm (WF) uses mathematical derivation to solve the optimization problem, which requires multiple Lagrangian optimization solutions. In order to reduce the computational complexity and accelerate the convergence speed, a particle swarm optimization (PSO-WF) algorithm for water injection is proposed. It randomly selects particles in the target area, calculates the fitness, and gradually approximates the optimal solution to avoid negative power distribution. The simulation results show that, compared with the average power allocation scheme, the algorithm improves the system capacity and operation efficiency by about 80%; Compared with WF algorithm, it greatly reduces the complexity.

Multi-Target Adaptive Radar Waveform Design Based on SINR

Aiming at the radar waveform design problem of detecting multiple targets in the presence of clutter, a novel algorithm based on linear probability-weighted summation (PWS) of multi-target impulse response of signal to interference plus noise ratio (SINR) is proposed. In view of the traditional water-filling algorithm, the multi-target detection algorithm is further studied in a new way to improve the overall performance of the system. This method uses Jensen’s inequality to make a lot of derivations to determine the target function of the algorithm and the constraints. Simulation results show that the proposed algorithm has better detection performance and more accurate target information.

An Adaptive Multi-Target Radar Waveform Design Based on PWS Algorithm.

Due to the uncertainty of radar target prior information in actual scenes, waveform design based on radar target prior information cannot meet the requirements of detection performance and parameter estimation. Aiming at the problem of waveform design for detecting multi-target in the presence of clutter, a linear probability-weighted summation (PWS) algorithm based on multi-target impulse response is proposed and includes the radar waveform design based on mutual information (MI) and signal-to-interference ratio (SINR) criteria. In view of the traditional water-filling algorithm, the problem of multi-target is further investigated in a new way to improve the overall performance of the system. The method makes a lot of deductions by using Jensen’s inequality, to determine the algorithm objective function and energy constraint. The simulation results show that the proposed algorithm has better detection performance and more accurate target information.

The research on multiuser resource allocation optimization in cognitive radio

Spectrum resource allocation optimization is still a research hotspot in the field of cognitive radio. The optimization framework based on Bandwidth-Power Product (BPP) proposed in recent years provides a new direction for this field. Compared with the traditional waterfilling algorithm, the new optimization framework has effectively improved the utilization of spectrum resources. However, there are few further studies on BPP in recent years. Based on the work of predecessors, this paper improves the criterion of BPP by taking into account the impact of the multiplexing of spectrum resource in different spatial locations on the spectrum utilization. The simulation results show that the improved BPP framework can further improve the utilization of spectrum resource; besides, the framework is more in line with the actual cognitive radio environment.

Service-Oriented Power Allocation for High-Speed Railway Wireless Communications

The rapid development of high-speed railways (HSRs) all over the world is drawing much more attention on high-mobility wireless communication. For the wireless links that connect the passengers on the train to the cellular network, it is very essential to employ an appropriate power allocation strategy to guarantee the reliability and efficiency of information transmission. Therefore, this paper concentrates on evaluating the transmission performance of wireless links in the HSRs and attempting to derive an optimal power allocation strategy of this scenario. Considering the fact that the information transmitted between the train and the base station usually has diverse quality-of-service (QoS) requirements for various services, a QoS-based achievable rate region is utilized to characterize the transmission performance in this paper to instead of traditional single throughput. Based on it, a QoS-distinguished power allocation algorithm is derived to achieve the largest achievable rate region. It is proved that the traditional water-filling algorithm and the channel inverse algorithm can be regarded as two specific cases of this new algorithm. Besides, the robust performance of the proposed strategy is also discussed in detail under a non-uniform motion scenario, and its relative performance loss is evaluated in terms of energy consumption. Finally, we present a typical implementation example in a Rice fading environment when the data rate requirements are prior known.

Iterative Dynamic Water-Filling for Fading Multiple-Access Channels With Energy Harvesting

In this paper, we develop optimal energy scheduling algorithms for $N$-user fading multiple-access channels with energy harvesting to maximize the channel sum-rate, assuming that the side information of both the channel states and energy harvesting states for $K$ time slots is known {\em a priori}, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with ${\cal O}(NK)$ constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver is exponential in the number of constraints. This paper gives an efficient energy scheduling algorithm, called the iterative dynamic water-filling algorithm, that has a computational complexity of ${\cal O}(NK^2)$ per iteration. For the single-user case, a dynamic water-filling method is shown to be optimal. Unlike the traditional water-filling algorithm, in dynamic water-filling, the water level is not constant but changes when the battery overflows or depletes. An iterative version of the dynamic water-filling algorithm is shown to be optimal for the case of multiple users. Even though in principle the optimality is achieved under large number of iterations, in practice convergence is reached in only a few iterations. Moreover, a single iteration of the dynamic water-filling algorithm achieves a sum-rate that is within $(N-1)K/2$ nats of the optimal sum-rate.

Research on Bit and Power Allocation for MIMO Relay System

Abstract Bits and power allocation is an important issue of adaptive resource allocation in MIMO-OFDM system. In this paper, an improved algorithm, bits and power allocation algorithm based on waterfilling theorem, is proposed and used to the single-user MIMO amplify-and-forward relay system. The algorithm which adopts the method of bisection to search water-filling line can reduce the number of iterations with low complexity compared with the traditional water-filling algorithm. Simulation results show that system capacity and bit error rate performance can be improved effectively.