MIMO systems

The aim of this dissertation is to develop a comprehensive framework for designing optimal AI/ML-driven waveform solutions to achieve autonomous interference avoidance in fixed frequency bands. In the age of advanced wireless communications, minimizing interference is critical for maximizing the signal-to-interference-plus-noise ratio (SINR), particularly in densely occupied frequency environments. The research presented here focuses on developing adaptive MIMO waveform optimization techniques that dynamically adjust to varying interference conditions, enhancing communication reliability and performance for future autonomous machine-to-machine (M2M) networks. In addition to the established adaptive MIMO waveform optimization techniques, this dissertation investigates the implementation of AI-enhanced methods, to improve real-time adaptability in interference-rich environments. By leveraging neural networks, the framework enables the MIMO system to autonomously learn optimal waveform adjustments, providing resilience and efficiency under unpredictable interference conditions. This approach is validated through extensive simulations and experimental setups, demonstrating significant gains in SINR and overall communication reliability, marking a robust advancement toward achieving fully autonomous interference-avoiding communication in 6G and beyond networks. The AI-driven techniques further enhance the adaptability of the MIMO system across diverse interference scenarios, contributing to more consistent performance. These improvements offer a scalable approach for interference avoidance, adaptable to various network configurations.

The underwater channel poses numerous challenges for acoustic communication.
Acoustic waves suffer long propagation delay, multipath, fading, and potentially
high spatial and temporal variability. In addition, there is no typical underwater
acoustic channel; every body of water exhibits quantifiably different properties. Underwater
acoustic modems are traditionally operated at low frequencies. However, the
use of broadband, high frequency communication is a good alternative because of the
lower background noise compared to low-frequencies, considerably larger bandwidth
and better source transducer efficiency. One of the biggest problems in the underwater
acoustic communications at high frequencies is time-selective fading, resulting
in the Doppler spread. While many Doppler detection, estimation and compensation
techniques can be found in literature, the applications are limited to systems operating
at low frequencies contained within frequencies ranging from a few hundred Hertz
to around 30 kHz.

Channel assignment in multi-radio networks is a topic of great importance because
the use of multiple channels and multiple radios reduces interference and increases the
network throughput. The goal of our research is to design algorithms that maximize the
use of available resources while providing robustness to primary users that could reclaim
one or more channels. Our algorithms could be used in ad hoc networks, mesh networks,
and sensor networks where nodes are equipped with multiple radios. We design
algorithms for channel assignment which provide robustness to primary users without
assuming an accurate primary user behavior model. We also compute bounds for capacity
in grid networks and discuss how the capacity of a network changes when multiple
channels are available. Since preserving energy is very important in wireless networks,
we focus on algorithms that do not require powerful resources and which use a reduced
number of messages.

High data rate acoustic communications become feasible with the use of communication systems that operate at high frequency. The high frequency acoustic transmission in shallow water endures severe distortion as a result of the extensive intersymbol interference and Doppler shift, caused by the time variable multipath nature of the channel. In this research a Single Input Multiple Output (SIMO) acoustic communication system is developed to improve the reliability of the high data rate communications at short range in the shallow water acoustic channel. The proposed SIMO communication system operates at very high frequency and combines spatial diversity and decision feedback equalizer in a multilevel adaptive configuration. The first configuration performs selective combining on the equalized signals from multiple receivers and generates quality feedback parameter for the next level of combining.

This thesis presents the development of a Multiple-Input Multiple-Output (MIMO) capable high bit rate acoustic modem operating at high frequencies. A MIMO channel estimation technique based on Least-Squares (LS) estimation is developed here. Channel deconvolution is completed using a Minimum Mean-Square Error (MMSE) Linear Equalizer (LE). An Interference Cancellation Linear Equalizer (ICLE) is used to provide the theoretical limit of the MIMO deconvolution process. The RMSE of the channel estimation process was 1.83 % and 6.1810 %, respectively for simulated and experimental data. Using experimental data, the RMSE before MIMO deconvolution process was 141.3 % and dropped down to 60.224 % and to 4.4545 %, respectively after LE and ICLE. At raw reception, the RMSE was 101.83 % and dropped down to 9.36 % and to 1.86 % using respectively LE and ICLE with simulated data.