# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 1 Introduction and Definitions
The main tradeoffs between simultaneously achieving good sensing and communication metrics are derived from the intrinsic differences between communication and sensing/radar signals [26]: for example, communication signals typically include both pilots (unmodulated data sequences) and modulated data signals (carrying the user information), while radar signals are traditionally unmodulated signals; most communication systems nowadays rely on packet-based/discontinuous transmission, while continuous sensing requires continuous transmissions; front-end communication receivers need low noise figures, while the radar receivers have more relaxed noise-figure requirements, etc. The SLAM terminology, which basically refers to joint positioning of targets and the sensing and positioning of the environment (walls, scatterers, other objects, persons, cars, etc.) is much older than ISAC terminology and was first used in robotics research about four decades ago. One of the first comprehensive tutorials about SLAM, also addressing  
Figure 1: A Venn diagram of the three converging areas and existing terminology, also including the new JPSAC term; all abbreviations are explained later on in this paper and can be found in the list of abbreviations. a bit of the history of SLAM, is the Durrant-Whyte and Bailey tutorial [30] from 2006. At that time, the focus was fully on mobile robots equipped with various sensors, including cameras/vision sensors. The concept of radio SLAM (or radio-based SLAM) is much more recent (first appearing only a couple of years ago) and refers to SLAM methods relying purely on radio signals (without the need for visual sensors). A few of the top recent works on radio SLAM are as follows: Ref. [31] (presenting a 2023 survey on radio SLAM), Ref. [32] (presenting in 2022 several radio SLAM filters for reduced receiver complexity), Ref. [33] (a very recent work published in 2025 on RIS-based SLAM), Ref. [34] (presenting an experimental validation of a radio SLAM full-processing chain on a wireless receiver in 2024), and Ref. [35] (proposing a robust snapshot-radio SLAM solution for mixed environments with Line of Sight (LOS) and Non-Line of Sight (NLOS) conditions in 2024). There are three main types of sensing and, thus, three main types of SLAM [29], listed as follows:  
* _Monostatic SLAM_: When the transmitter and receiver are co-located on the same device (thus, they also share the same clock), the sensing part can rely on reflections on the surroundings sent by the device acting as a transmitter and captured by the same device, acting also as a receiver; the'sensed' targets can be _disconnected_, meaning that they do not need to be equipped with a transmitter or a receiver themselves. A _connected_ device refers to a device equipped with radio connectivity. Typically, with radio-based SLAM, monostatic SLAM is performed at the base-station side (which has more computational power than the User Equipment (UE)); the term monostatic SLAM did originate from robotics, wherein a robot performed monostatic SLAM based on vision signals [29]. * _Bistatic SLAM_: When there are two separate devices of interest, one acting as the transmitter (e.g., the base station) and the other one acting as the receiver (e.g., UE), receiving signals both from the LOS path (these control the part responsible for the positioning of the device) and from the NLOS path (i.e., reflections on the environment objects, which enable the sensing or location of those objects), single or multiple reflections on the environmental objects are possible. * _Multistatic SLAM_ is the most generic case, covering both of the previous cases as well, and it refers to situations involving multiple transmitters and receivers that transmit pilot signals for location and sensing. In bi- and multistatic configurations, the synchronization of time and frequency between the different devices, acting as transmitters and receivers, is not a given; thus, the SLAM algorithms should also take into account the clock and frequency inaccuracies.