Designing photonic structures and obtaining optimal responses is still a difficult task. A large number of gradient based algorithms and adjoint solvers are used, and the results have been effective in that complex free form geometries of devices are capable of being created. The fact remains, however, that many solvers still tend to be black boxes, and there is very little transparency in how the solver comes to its conclusion. Furthermore, because many of these solvers are gradient based, additional knowledge of the device is needed, otherwise the solver can get trapped in a local minimum and not reach the true optimal geometry. To this end, an inverse design framework that combines adjoint optimization, automated machine learning (AutoML), and explainable artificial intelligence (XAI) is presented in order to determine both the relation between device structure and performance and also minimize the effect of local minima trapping. This framework is used in the inverse design of waveguides and achieves an average of 43\% increase in performance. Consequently, this study portrays how to extend beyond traditional inverse design solvers in terms of both performance and ability to elucidate the solvers' decisions.

Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems. In massive MIMO, a base station (BS) is equipped with a large antenna with potentially hundreds of antennas elements, allowing many users to be served simultaneously. Unfortunately, the hardware complexity and power consumption will scale with the number of antennas. The use of one-bit analog-to-digital converters (ADCs) provides an attractive solution to solve the above issues, since a one-bit ADC consumes negligible power and complex automatic gain control (AGC) can be removed.

However, the signal distortion from the severe quantization poses significant challenges to the system designer. One bit quantization effectively removes all amplitude information, which is not recoverable by an increase in signal strength. This places a bound on channel estimation performance. Since the channel model is highly nonlinear, linear detector is suboptimal compared to more sophisticated nonlinear techniques.

To reduce the impairment caused by one-bit quantization, a novel antithetic dithering scheme is developed. Antithetic dither is introduced into the system to generate negative correlated noise. Efficient channel estimation algorithms are developed to exploit the induced negative correlated noise in the system. A statistical framework is developed to validate the noise reduction from negative correlated quantized output.

To improve the performance of data detection, feed forward neural network based detectors are developed, performance of these detectors are analyzed, architectural modification and training techniques are employed to partially resolve issues that prevent the networks from reaching ideal maximum likelihood performance.

Next, model based approaches are evaluated and the shortcomings of iterative methods that rely on the exact likelihood are identified. Iterative methods based on the exact likelihood is shown to diverge due to the increasingly large gradient at high SNR. The constant gradient induced by the sigmoid approximation is shown to increase the robustness of these methods. A structured deep learning detector based on stochastic variational inference is proposed. Stochastic estimate of the gradient is introduced to reduce complexity of the algorithm. Damping is added to improve the performance of mean field inference. Parallel processing is proposed to reduce inference time. The proposed detector is shown to outperform existing methods that do not employ a second candidate search step.