An Efficient Machine Learning Based Precoding Algorithm for Millimeter-Wave Massive MIMO

Millimeter wave communication works in the 30–300 GHz frequency range,and can obtain a very high bandwidth,which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation(5G).The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture.The resulting array gain can compensate for the path loss of the millimeter wave.Utilizing this feature,the millimeter wave massive multiple-input multiple-output(MIMO)system uses a large antenna array at the base station.It enables the transmission of multiple data streams,making the system have a higher data transmission rate.In the millimeter wave massive MIMO system,the precoding technology uses the state information of the channel to adjust the transmission strategy at the transmitting end,and the receiving end performs equalization,so that users can better obtain the antenna multiplexing gain and improve the system capacity.This paper proposes an efficient algorithm based on machine learning(ML)for effective system performance in mmwave massive MIMO systems.The main idea is to optimize the adaptive connection structure to maximize the received signal power of each user and correlate the RF chain and base station antenna.Simulation results show that,the proposed algorithm effectively improved the system performance in terms of spectral efficiency and complexity as compared with existing algorithms.

Learning to optimize:A tutorial for continuous and mixed-integer optimization

Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimization problems frequently share common structures,L2O provides a tool to exploit these structures for better or faster solutions.This tutorial dives deep into L2O techniques,introducing how to accelerate optimization algorithms,promptly estimate the solutions,or even reshape the optimization problem itself,making it more adaptive to real-world applications.By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand,this tutorial provides a comprehensive guide for practitioners and researchers alike.

Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.