Self-Optimizing Near and Far-Field MIMO Transmit Waveforms

We consider the problem of dynamically optimizing a multiple-input multiple-output (MIMO) wireless waveform in a given potentially heavily utilized fixed frequency band with applications in near-field or far-field autonomous machine-to-machine communications. In particular, we find the transmitter beam weight vector and the pulse code sequence that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR joint space-time receiver filter. We propose and derive two novel model-based solutions: (a) Disjoint, space first (transmit weight vector) then time (pulse code sequence) waveform optimization and (b) jointly optimal transmit weight vector and pulse code sequence optimization (a mixed integer programming problem.) The proposed formally derived algorithmic solutions are studied in extensive simulations under varying waveform code length, near-field/far-field and spread-spectrum/non-spread-spectrum interference, in light and dense interference scenarios. Our findings highlight the effectiveness of the described methods compared to static conventionally designed MIMO links and the remarkable ability of the joint space-time optimized waveforms to avoid heavy interference.