Application of Machine Learning to Multi Antenna Transmission and Machine Type Resource Allocation

Wireless communication systems is a well-researched area in electrical engineering that has continually evolved over the past decades. This constant evolution and development have led to well-formulated theoretical baselines in terms of reliability and efficiency. However, most communication baselines are derived by splitting the baseband communications into a series of modular blocks like modulation, coding, channel estimation, and orthogonal frequency modulation. Subsequently, these blocks are independently optimized. Although this has led to a very efficient and reliable process, a theoretical verification of the optimality of this design process is not feasible due to the complexities of each individual block. In this work, we propose two modifications to these conventional wireless systems. First, with the goal of designing better space-time block codes for improved reliability, we propose to redesign the transmit and receive blocks of the physical layer. We replace a portion of the transmit chain - from modulation to antenna mapping with a neural network. Similarly, the receiver/decoder is also replaced with a neural network. In other words, the first part of this work focuses on jointly optimizing the transmit and receive blocks to produce a set of space-time codes that are resilient to Rayleigh fading channels. We compare our results to the conventional orthogonal space-time block codes for multiple antenna configurations.

The second part of this work investigates the possibility of designing a distributed multiagent reinforcement learning-based multi-access algorithm for machine type communication. This work recognizes that cellular networks are being proposed as a solution for the connectivity of machine type devices (MTDs) and one of the most crucial aspects of scheduling in cellular connectivity is the random access procedure. The random access process is used by conventional cellular users to receive an allocation for the uplink transmissions. This process usually requires six resource blocks. It is efficient for cellular users to perform this process because transmission of cellular data usually requires more than six resource blocks. Hence, it is relatively efficient to perform the random access process in order to establish a connection. Moreover, as long as cellular users maintain synchronization, they do not have to undertake the random access process every time they have data to transmit. They can maintain a connection with the base station through discontinuous reception. On the other hand, the random access process is unsuitable for MTDs because MTDs usually have small-sized packets. Hence, performing the random access process to transmit such small-sized packets is highly inefficient. Also, most MTDs are power constrained, thus they turn off when they have no data to transmit. This means that they lose their connection and can't maintain any form of discontinuous reception. Hence, they perform the random process each time they have data to transmit. Due to these observations, explicit scheduling is undesirable for MTC.

To overcome these challenges, we propose bypassing the entire scheduling process by using a grant free resource allocation scheme. In this scheme, MTDs pseudo-randomly transmit their data in random access slots. Note that this results in the possibility of a large number of collisions during the random access slots. To alleviate the resulting congestion, we exploit a heterogeneous network and investigate the optimal MTD-BS association which minimizes the long term congestion experienced in the overall cellular network. Our results show that we can derive the optimal MTD-BS association when the number of MTDs is less than the total number of random access slots.