Tone Reservation-Based PAPR Reduction Using Manifold Optimization for OFDM-ISAC Systems

We consider the peak-to-average power ratio (PAPR) reduction challenge of orthogonal frequency division multiplexing (OFDM) systems utilizing tone reservation (TR) under a sensing-enabling constraint, such that the signals placed in the reserved tones (RTs) can be exploited for Integrated Sensing and Communication (ISAC). To that end, the problem is first cast as an unconstrained manifold optimization problem, and then solved via an iterative projected gradient descent algorithm assisted by an approximation of the infinity norm. Simulation results show that the proposed method, while maintaining a level of PAPR reduction similar to state of the art (SotA), not only has lower computational complexity but also outperforms the alternatives in terms of sensing performance.

Optimized Tone Reservation for Opportunistic OFDM Communications and Sensing

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems via optimized sparsification of tone reservation (TR). In particular, we propose a novel TR optimization method in which the minimum number of effectively used peak-reserved tones (PRTs) required to satisfy a prescribed PAPR level is found, leaving the remaining PRTs free to be opportunistically utilized by other functionalities, such as joint communication and sensing (JCAS), index modulation (IM), cognitive radio (CR) and others. The proposed method relies on an l0 norm regularization approach to penalize the number of PRTs, leading to a problem convexized via fractional programming (FP), whose solution is shown to ensure that the prescribed PAPR is achieved with high probability with a smaller number of PRTs than state of the art (SotA) methods. The contribution can be seen as a mechanism to enable the opportunistic integration of adjacent functionalities into existing OFDM-based systems.