The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.
Pilot and channel state information (CSI) feedback overhead in the downlink and uplink paths are two major implementation challenges in frequency-division duplex (FDD) based massive MIMO systems. When the massive MIMO channel satisfies the burst-sparsity property, we can acquire the channel with compressed pilots and CSI feedback in a more efficient approach. This paper proposes a burst-form estimation approach, referred to as the burst-form least squares (BFLS) algorithm, to fully utilize the burst-sparsity property of massive MIMO channels. The proposed algorithm is based on knowledge of the starting location of each burst at the user side. For situations where the starting locations change quickly or are otherwise initially unknown at the user, a starting point estimation (SPE) algorithm is proposed to provide the position of each burst in the channel vector. Numerical results demonstrate that the BFLS algorithm acquires the channel better than competing approaches and reaches the performance upper bound. It is shown that the SPE algorithm can find the location of bursts with high accuracy and using the estimated values do not significantly degrade the estimation quality.
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