Understanding the Convergence of Sensing and Communication using ISAC

Prof. Emil Björnson has been a regular feature on our blogs. Over the years he has covered a wide range of topics, often breaking down complex wireless concepts into something much easier to understand. His latest talk focuses on Introduction to Integrated Sensing and Communication, or ISAC, which is increasingly being positioned as a key capability for 6G.

At a high level, ISAC is about using the same wireless infrastructure not only to communicate data but also to sense and understand the surrounding environment. Today, communication and sensing largely exist as separate domains. Mobile networks are designed to transmit information reliably, while radar systems are designed to detect objects and measure their properties. What ISAC proposes is a convergence of these two worlds.

The starting point is a simple but powerful observation. When a wireless signal is transmitted, it does not just travel directly to the intended receiver. It also reflects off objects in the environment, creating multiple propagation paths. In communication systems, these reflections are typically treated as distortions that need to be mitigated. In sensing systems, however, these same reflections are the signal of interest.

This difference in perspective is fundamental. Communication systems encode information into the transmitted signal and try to remove the impact of the channel. Sensing systems do the opposite. They transmit known signals and analyse how the environment modifies them in order to extract information about objects, motion, and geometry.

Emil’s lecture also highlights that, despite being developed for different purposes, communication and radar systems already share a surprising amount of common ground. Similar antenna technologies are used in both domains, ranging from fixed arrays to more advanced electronically steerable arrays. The key difference lies in how they are optimised. Communication systems focus on data throughput and coverage, while radar systems prioritise detection accuracy and tracking capability.

To understand sensing more concretely, the lecture revisits the three classical radar features. These are range, velocity, and angle estimation. Range is obtained by measuring the delay between transmitted and received signals. Velocity is derived from Doppler shifts, where motion causes a change in frequency. Angle estimation is achieved through beam steering and spatial processing. Together, these allow systems not only to detect objects but also to track them over time.

While the underlying physics of propagation is similar for communication and sensing, there are important differences. Communication is typically a one way link, either uplink or downlink, with relatively moderate propagation loss. Radar, on the other hand, involves a two way path where the signal travels to a target and back again. This leads to much higher path loss, which explains why traditional radar systems often require significantly higher transmit power than cellular systems.

Another key difference lies in how the systems operate. Communication systems are usually half duplex, switching between transmission and reception. Radar systems, especially in monostatic configurations, often require full duplex operation where transmission and reception happen simultaneously. These differences create practical challenges when trying to integrate the two functionalities into a single system.

The lecture outlines three levels of integration that can be considered for ISAC. The simplest is site sharing, where communication and sensing systems are co-located but remain separate. A deeper level is hardware sharing, where the same equipment is used for both functions but not at the same time. The most advanced level is signal sharing, where the same waveform and resources are used simultaneously for communication and sensing. This last approach offers the greatest flexibility but also introduces the most complex trade-offs.

In the context of 6G, Emil suggests that sensing is likely to be introduced as an extension to communication rather than a completely separate capability. The primary function of the network will remain data transmission, but sensing can be added to enhance both network performance and external applications.

Internally, sensing can improve how networks operate. It can help track users even when they are not actively transmitting, enabling better beamforming and handover decisions. It can also support more efficient energy management by allowing parts of the network to switch off when no activity is detected. Interference management and positioning accuracy can also benefit from the additional environmental awareness.

Externally, the opportunities are arguably even more significant. ISAC can provide environmental data that can be exposed through network APIs to support a wide range of applications. These include smart cities, where traffic and crowd movement can be monitored, and smart factories, where robots and processes can be tracked in real time. There are also applications in surveillance, drone detection, and digital twins, where real world environments are mirrored in software for analysis and optimisation.

The lecture also provides useful insight into how sensing is actually performed in practice. Range estimation relies on measuring the round trip delay of signals and converting it into distance using the speed of light. Velocity estimation is based on Doppler shifts and depends on observing changes over time. Angle estimation uses spatial processing and beamforming to determine the direction of objects.

An important takeaway is that there are inherent trade-offs in waveform design. For example, signals that are well suited for estimating range are not necessarily optimal for estimating velocity. This creates challenges when trying to design a single waveform that can support both communication and sensing effectively.

This is where modern communication techniques such as OFDM become particularly interesting. OFDM already uses structured signals across time and frequency, including reference signals that are known at the receiver. These can be repurposed for sensing tasks, allowing range and velocity estimation to be integrated into existing communication frameworks.

Overall, the lecture provides a clear and grounded introduction to ISAC. It avoids the hype that often surrounds 6G topics and instead focuses on the underlying principles, practical challenges, and realistic opportunities.

ISAC is unlikely to replace traditional sensing systems in all scenarios, especially where very high power or specialised capabilities are required. However, its value lies in turning the existing communication infrastructure into a distributed sensing platform. If implemented effectively, this could significantly enhance both network performance and the range of services that mobile networks can support in the future.

The video of the lecture is as follows and the slides are available here:

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