# Exploring ISAC: Information-Theoretic Insights
## 1 Introduction
Keeping the model and results general allows us to derive broadly based conclusions and also cover a larger range of application scenarios. Specifically, in this article, we start with a a brief historical perspective of ISAC (Section 2), followed by a first technical section (Section 3) that considers a canonical ISAC point-to-point setup with a single Tx wishing to communicate to a single Rx and where the sensing task is to estimate a state sequence (such as the accelerations of an obstacle) up to a desired distortion. We will start by discussing results on simple memoryless channel models, and then move on to very general models with memory. All the results discussed in this Section 3 illustrate the inherent tradeoff between the sensing and communication tasks encountered in such ISAC systems, while Section 3 considers a mono-static radar setup, i.e., the sensing task is performed at the Tx of the communication system, the subsequent section, Section 4, considers bi-static radar models where sensing is performed at the Rx. Section 5 further generalizes the setup to multiple Txs or Rxs and sensing at multiple terminals. Not only is the communication problem of formidable difficulty in these setups but the sensing task is also significantly more involved, as now collaborative and interactive sensing strategies can be applied to provide remote terminals with sensing information gathered at other terminals. As we shall see, in such scenarios, it does not just suffice to exchange sensing information using standard communication schemes, but instead the code construction previously only used for data communication now needs to be adapted to also enable the collaborative sensing tasks. Moreover, given the distributed sensing information that has to be conveyed from certain terminals to others, network joint source-channel coding schemes become essential to attain good sensing performances. The subsequent Section 6, then describes how the above ISAC schemes and performance limits need to be adapted so as to ensure secrecy of only messages or of states and sensing targets/states. The last technical section (Section 7) of this overview then takes a different approach to the sensing task, assuming that the sensing task consists of detecting a single parameter (and not estimating a state vector as in the previous chapters) which determines the behavior of the sensing target. This problem seems to be slightly more challenging but first instructive results are presented, in particular, when the Tx is restricted to non-adaptive coding schemes where the backscattered signals can be used for the sensing task but not to produce the subsequent inputs. The overview article is then concluded with concluding remarks. There exists a large body of studies on other aspects of ISAC systems, for example, the works [44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55] have studied ISAC from a more communication-theoretic perspective in environments where Txs and Rxs are equipped with multiple antennas, in particular, in so-called massive multi-input and multi-output (MIMO) systems. Interesting research directions result in these MIMO systems regarding whether the smart selection of beamformers allows a reduction in the tradeoff between communication and sensing performances and how the tradeoff is influenced by the choice of the antenna distance. Recent initiatives to improve the understanding of ISAC systems of course also include learning-based studies. The tutorial in [41] provides a comprehensive overview of works using deep-learning based techniques and the summary of the reviewed results are provided in Table 1.