Joint Communications and Sensing Employing Optimized MIMO-OFDM Signals

Joint communications and sensing (JCAS) have the potential to improve the overall energy, cost and frequency efficiency of Internet of Things (IoT) systems. As a first effort, we propose to optimize the MIMO-OFDM data symbols carried by subcarriers (SCs) for better time- and spatial-domain signal orthogonality. This can reduce intertarget and interantenna interference, enabling high-quality sensing. We establish an optimization problem that modifies data symbols on SCs to enhance the above-mentioned signal orthogonality. We also develop an efficient algorithm to solve the problem based on the majorization-minimization framework. Moreover, we discover unique signal structures and features from the newly modeled problem, which substantially reduce the complexity of majorizing the objective function. We also develop new projectors to enforce the feasibility of the obtained solution. Simulations show that to achieve the same sensing performance, the optimized waveform can reduce the signal-to-noise ratio (SNR) requirement by 3-4.5 dB compared with the original waveform, while the SNR loss for the uncoded bit error rate is only 1-1.5 dB.