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We modeled and investigated the silicon microring modulator (SMM) based on a PIN junction. The proposed model's results are in good agreement with the experimental results. The effects of various parameters were considered in an optimized model to decrease power consumption as well as increase the bandwidth and extinction ratio of the SMM. The power consumption parameter plays a key role in the modulator design. To decrease this parameter, modulator dimensions were optimized using a genetic algorithm. This was done by considering the effect of the quality factor on the gradient change of the SMM transmission profile. In comparison with experimental results, the power consumption in the presented optimized model decreased dramatically from 9.7 to 0.042 μW. In addition, the extinction ratio and bandwidth increased by 12.5% and 50%, respectively. [ABSTRACT FROM AUTHOR]

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction. [ABSTRACT FROM AUTHOR]