Meeting IMT 2030 Performance Targets: The Potential of OTFDM Waveform and Structural MIMO Technologies

The white paper focuses on several candidate technologies that could play a crucial role in the development of 6G systems. Two of the key technologies explored in detail are Orthogonal Time Frequency Division Multiplexing (OTFDM) waveform and Structural MIMO (S-MIMO).

Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning

In the domain of algorithmic music composition, machine learning-driven systems eliminate the need for carefully hand-crafting rules for composition. In particular, the capability of recurrent neural networks to learn complex temporal patterns lends itself well to the musical domain. Promising results have been observed across a number of recent attempts at music composition using deep RNNs. These approaches generally aim at first training neural networks to reproduce subsequences drawn from existing songs. Subsequently, they are used to compose music either at the audio sample-level or at the note-level. We designed a representation that divides polyphonic music into a small number of monophonic streams. This representation greatly reduces the complexity of the problem and eliminates an exponential number of probably poor compositions. On top of our LSTM neural network that learnt musical sequences in this representation, we built an RL agent that learnt to find combinations of songs whose joint dominance produced pleasant compositions. We present Amadeus, an algorithmic music composition system that composes music that consists of intricate melodies, basic chords, and even occasional contrapuntal sequences.

Sampling Based Approaches to Handle Imbalances in Network Traffic Dataset for Machine Learning Techniques

Network traffic data is huge, varying and imbalanced because various classes are not equally distributed. Machine learning (ML) algorithms for traffic analysis uses the samples from this data to recommend the actions to be taken by the network administrators as well as training. Due to imbalances in dataset, it is difficult to train machine learning algorithms for traffic analysis and these may give biased or false results leading to serious degradation in performance of these algorithms. Various techniques can be applied during sampling to minimize the effect of imbalanced instances. In this paper various sampling techniques have been analysed in order to compare the decrease in variation in imbalances of network traffic datasets sampled for these algorithms. Various parameters like missing classes in samples, probability of sampling of the different instances have been considered for comparison.