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Euclidean distance optimized antenna selection (EDAS) can significantly improve the bit-error-rate (BER) performance of spatial modulation (SM) systems. However, the exhaustive search over all possible antenna subsets leads to inherently high-search complexity. In this letter, a cross-entropy-based antenna selection (CEAS) scheme is proposed, by formulating the antenna selection problem in SM as a combinatorial optimization one. Simulation results show that the proposed CEAS scheme achieves considerable reduction in search complexity, while approaching the optimal performance of exhaustive search.

Considering the defects of the Distance Vector-Hop (DV-Hop) localization algorithm making errors and having error accumulation in wireless sensor network (WSN), we proposed a new DV-Hop localization algorithm based on half-measure weighted centroid. This algorithm followed the two-dimensional position distribution, designed the minimum communication radius, and formed a reasonable network connectivity firstly. Then, the algorithm corrected the distance between the beacon node and its neighbour node to form a more accurate jump distance so that the shortest path can be optimized. Finally, we theorized the proposed localization algorithm and verified it in simulation experiments, including same communication radius, different communication radii, and different node densities in same communication radius, and have compared the localization error and localization accuracy, respectively, between the proposed algorithm and the DV-Hop localization algorithm. The experiment’s result shows that the proposed localization algorithm have reduced the localization’s average error and improved the localization’s accuracy.

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