Uplink-Downlink Duality for Beamforming in Integrated Sensing and Communications
For researchers in ISAC, this provides a novel theoretical framework and algorithm for joint beamforming optimization, though the problem is domain-specific.
This paper tackles beamforming and power optimization for integrated sensing and communications (ISAC), minimizing the Bayesian Cramér-Rao bound while satisfying communication constraints. It extends uplink-downlink duality to ISAC, enabling an efficient iterative algorithm.
This paper considers the beamforming and power optimization problem for a class of integrated sensing and communications (ISAC) problems that utilize the communication signals simultaneously for sensing. We formulate the problem of minimizing the Bayesian Cramér-Rao bound (BCRB) on the mean-squared error of estimating a vector of parameters, while satisfying downlink signal-to-interference-and-noise-ratio constraints for a set of communication users at the same time. The proposed optimization framework comprises two key new ingredients. First, we show that the BCRB minimization problem corresponds to maximizing beamforming power along certain sensing directions of interest. Second, the classical uplink-downlink duality for multiple-input multiple-output communications can be extended to the ISAC setting, but unlike the classical communication problem, the dual uplink problem for ISAC may entail negative noise power and needs to include an extra condition on the uplink beamformers. This new duality theory opens doors for efficient iterative algorithm for optimizing power and beamformers for ISAC.