6G technology advances wireless communication by offering highly personalized user experiences through the integration of precise radar systems for accurate localization. This capability allows for the seamless merging of location-based services with communication networks, enhancing service delivery and network efficiency. Known as integrated sensing and communication (ISAC), this approach leverages the synergy between radar and communication technologies to optimize connectivity.
Understanding ISAC is crucial for grasping how 6G will enhance network performance and user experience through tailored communication solutions. Emerging applications such as autonomous vehicles, smart cities, and advanced healthcare systems will also require the seamless integration of high-speed data transmission and precise environmental sensing. These applications, among others, underscore the necessity of developing ISAC technology to meet the demands of a highly interconnected and intelligent future.
Radar and communication integration has been studied from various perspectives, including Wi-Fi®, military multifunction radar, and automotive applications. Additionally, the level of integration between the two can vary, encompassing concepts like design in the waveform and spatial domains. Other design choices including RF architecture, beamforming, appropriate channel models, and data-driven AI algorithms need to be considered for accurate, high-resolution sensing. An integrated environment for joint investigation of these elements is essential.
In this context, MATLAB® emerges as an indispensable tool for ISAC research, offering complete workflows and a set of products targeted for exploring and developing integrated communication and sensing technologies. MATLAB is an intuitive platform for simulating scenarios, designing and testing algorithms, and analyzing data. It helps researchers speed up development, validate designs efficiently, and advance 6G goals.
This white paper will examine ISAC paradigms and research efforts as well as ISAC applications. First, some key technical terms will be defined.
Radar and communication coexistence involves using spectrum sensing to manage overlapping frequencies between radar and communication systems. Radar and communication co-design integrates communication and sensing tasks, either through shared physical space with separate hardware or by using shared hardware and waveforms.
Passive leveraging of communication signals utilizes existing communication signals’ channel estimates to detect and infer the movement and position of objects without dedicated sensing hardware.
ISAC research efforts go in three main directions depending on the level of integration of sensing and communication functionalities: coexistence, co-design, and passive leveraging of signals.
Radar and Communication Coexistence
As 5G NR systems and future 6G systems expand into higher frequency ranges beyond those used in LTE, managing the spectrum becomes increasingly complex. These higher frequency ranges have traditionally been utilized by radar systems. Consequently, the spectrum used by radar and wireless communication systems may overlap, necessitating spectrum sharing. This situation requires future radar and wireless communication systems to incorporate spectrum sensing to detect occupied frequencies and avoid conflicts. Further, the push for broader 5G coverage is fueled by the advantages of higher data rates and reduced latencies. This expansion requires new 5G base stations, which in turn necessitates understanding the impact of these signals on existing systems operating in adjacent frequency bands, such as air traffic control radar.
For successful coexistence, two important aspects need to be considered: spectrum sensing to tell which systems are present in the spectrum and their location and interference analysis to assess how one system interacts with another when they share the same frequency band.
MATLAB offers integrated workflows for simulating complex scenarios across 5G, radar, deep learning, phased arrays, and scenario modeling. Key coexistence scenarios include spectrum sensing and interference analysis. For example, MATLAB facilitates spectrum sensing with workflows that use a semantic segmentation neural network trained on synthesized radar and wireless communication signals. This neural network can detect radar and wireless communication signals within the same received spectrum. Additionally, MATLAB allows for modeling scenarios such as air traffic control radar operating near a 5G base station, helping to analyze the impact of 5G signals on radar signal reception.
Radar and Communication Co-Design
Co-design focuses on creating systems that perform both communication and sensing tasks simultaneously. Integration could be at a loose level, where functionalities share physical space but use separate hardware and waveforms. Integration could also be at a tighter scale, where most hardware is shared, and the same waveform serves both purposes. In tightly integrated systems, which are the focus of this section, system design can follow a communication-centric approach, that is, using communication signals for radar. Design can also take a radar-centric approach—embedding communication data within radar waveforms.
Co-Designing the Waveforms
In a radar-centric approach, radar waveforms are used with embedded communication data, while in a communication-centric approach, the echo from an OFDM signal is utilized for sensing purposes. MATLAB facilitates the exploration of waveform co-design with an end-to-end workflow using two approaches. The first approach uses a typical radar waveform, PMCW, while the second uses OFDM, a standard communication waveform for both functionalities.
![Plots showing a modulated radar and a transmitted waveform in time for each pulse.](https://la.mathworks.com/campaigns/offers/next/6g-isac-with-matlab/_jcr_content/mainParsys/band/mainParsys/lockedsubnav/mainParsys/columns_2104889913/d472ea8c-2b85-4a94-8564-b144f207b08a/image.adapt.full.medium.jpg/1730831981980.jpg)
MATLAB enables the co-design of radar and communication waveforms.
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Joint Radar-Communication Using PMCW and OFDM Waveforms
This example simulates the transmission, propagation, and receiving of the waveforms by both the radar receiver and the downlink user. The workflow evaluates both waveforms for both functionalities and investigates several common performance metrics.
Co-Design in the Spatial Domain
Other concepts of ISAC explore the spatial processing dimension. Commonly explored scenarios include having a multi-antenna base station with some beams serving radar and others serving communication needs. Another possibility is that the two functionalities share the beam, with radar using the main lobe and communication data using the side lobes.
MATLAB lets you design novel waveforms for a dual-function MIMO system in the spatial domain as shown below, where different beams can be formed to realize different functionalities.
![A diagram of a radar-communication system where radar beams and communications channels are shown serving the same geographical area.](https://la.mathworks.com/campaigns/offers/next/6g-isac-with-matlab/_jcr_content/mainParsys/band/mainParsys/lockedsubnav/mainParsys/columns_388025848/41aacda2-dba1-41f6-ba23-6da206a364e0/image.adapt.full.medium.jpg/1730832502893.jpg)
Spatial co-design for ISAC.
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Waveform Design for a Dual-Function MIMO RadCom System
This example demonstrates a workflow that uses a phased array to facilitate MIMO communication, taking advantage of waveform diversity to achieve good radar performance.
Passive Leveraging of Communication Signals
This ISAC paradigm leverages channel estimates at the receiver side to detect the presence of moving objects within a wireless environment. By analyzing variations in the channel state based on these moving objects, the system can infer their movement and position without the need for dedicated sensing hardware. Extracted channel estimates can be used to produce range-Doppler plots, which are instrumental in sensing movement. This approach is inherently passive, using existing communication signals and infrastructure without needing active emissions for sensing.
Another passive approach involves applying artificial intelligence to infer movement or detect presence using channel state information (CSI). For instance, one can combine a convolutional neural network (CNN) with CSI to sense human activity. An example from Wi-Fi includes capturing beacon frames from routers and training a CNN on these captures to detect human presence. This workflow can be extended to 5G and 6G signals.
MATLAB supports both approaches with robust tools for signal processing, machine learning, and data visualization. The extensive libraries and built-in functions in MATLAB allow for the efficient extraction and analysis of channel estimates, enabling the creation of range-Doppler plots. Additionally, MATLAB supports the development and training of CNNs, facilitating the integration of AI techniques to enhance sensing capabilities.
ISAC promises transformative applications across various domains by integrating radar and communication functionalities.
Radar and communication co-existence is particularly crucial in environments where spectrum scarcity is a significant challenge. For instance, in urban air mobility, co-existence strategies enable the safe operation of drones and air taxis by allowing them to share their spectrum with existing 5G networks, thereby optimizing air traffic management.
Radar and communication co-design opens new possibilities in smart automotive systems. Dual-functional radar-communication systems can enhance vehicle-to-everything (V2X) communications, enabling real-time data exchange and precise environmental sensing for autonomous driving. This co-design approach also finds applications in smart manufacturing, where integrated systems can monitor machinery health and communicate operational data simultaneously, boosting efficiency and enabling predictive maintenance.
Passive co-design (piggybacking) leverages existing wireless signals for environmental sensing, offering innovative solutions in smart home and healthcare settings. For example, passive Wi-Fi sensing can detect human presence and activity, enabling advanced home automation and elderly care by monitoring daily activities without the need for additional sensors. These paradigms collectively highlight the versatility and potential of ISAC technology in 6G, paving the way for a more connected, intelligent, and efficient future.
In designing ISAC systems, researchers typically focus on several critical components: radar and communication waveforms, channel models, and receiver hardware. Understanding and optimizing these elements are essential for developing efficient and accurate ISAC solutions. Radar and communication waveforms must be carefully designed to ensure they can simultaneously support both communication and sensing functionalities. Accurate channel models are crucial for predicting how signals propagate through various environments, while advanced receiver hardware is needed to process received signals. This section will explore these aspects, providing insights and guidance for researchers aiming to innovate in the field of ISAC.
Exploring Existing Waveforms
Exploring existing waveforms in radar and communication is essential for effective waveform design in ISAC systems. By leveraging well-established waveforms from both domains, researchers can identify potential synergies and tradeoffs that facilitate the dual functionality of ISAC. Understanding the strengths and limitations of these waveforms allows for informed decisions on how to adapt or combine them to meet the unique requirements of both communication and sensing.
Radar Waveforms
Radar systems can be classified into two categories:
- Pulsed radar
- Continuous wave (CW) radar
Pulsed radar emits high-power pulses in series and determines range using the time delay between the sent pulse and received echo, while velocity is calculated from changes in echo distance. MATLAB offers resources to learn more about pulsed radar as well as easily generate, analyze, and evaluate pulsed radar signals:
- Doppler estimation
- Simulation of test signals for radar receivers
- Range and Doppler estimation
- Performance analysis of pulsed, frequency-modulated, and phase-coded waveforms
On the other hand, continuous wave (CW) radar emits signals continuously, making it cost-effective and suitable for applications such as automotive and indoor wireless communication. Yet, it requires modulation for target location determination, with major classes including frequency-modulated (FMCW) and phase-modulated (PMCW) waveforms.
MATLAB helps you generate and analyze CW radar signals with powerful workflows and functions for:
- Waveform analysis using the ambiguity function
- Comparing ambiguity functions for different wave modulation schemes
- Basic radar models using phase-coded waveform
Communication Waveforms
MATLAB products enable you to create a broad spectrum of wireless waveforms, compliant with standards such as LTE, 5G, WLAN, Bluetooth®, and Satcom. The waveform generation capability in MATLAB allows you to generate standards-based and custom 5G/LTE signals that are not in the standard, as well as generic modulations such as OFDM, QAM, PSK, and various radar signals like FMCW and Linear FM. This enables precise simulation and testing across different communication protocols and testing conditions. By supporting both industry-standard and custom waveform generation, MATLAB waveform generators serve as a critical resource for those engaged in the development and analysis of ISAC systems. An excellent place to start is with the Wireless Waveform Generator app, which lets you generate standards-based waveforms with a few clicks.
Learn More
- Create Waveforms Using Wireless Waveform Generator App - Documentation
Channels
Accurate channel models are crucial ISAC systems, as they ensure reliable performance and efficiency in both communication and sensing functionalities. These models enable precise characterization of the propagation environment, accounting for factors such as path loss, multipath effects, and signal reflections. By providing a realistic representation of the channel, accurate models facilitate the optimization of signal processing algorithms, enhance system robustness, and improve the overall integration of communication and sensing capabilities.
Ray Tracing
Ray tracing models have demonstrated robust predictive performance for signal behavior at 60 GHz and at even higher frequencies and can be used as powerful models to build ISAC simulations. MATLAB has built-in functionality for ray tracing that can be combined with models for losses due to rain, terrain diffraction, refraction through the atmosphere, tropospheric scatter, and atmospheric. Additionally, ray tracing capabilities in MATLAB allow modeling signal attenuation as the signals bounce off specific material.
Learn More
- Ray Tracing Propagation Model - Documentation
- CDL Channel Model Customization with Ray Tracing - Example
- Indoor MIMO-OFDM Communication Link Using Ray Tracing - Example
![A ray tracing plot generated in MATLAB using the SiteViewer app featuring multiple signal paths between a transmitter and a receiver.](https://la.mathworks.com/campaigns/offers/next/6g-isac-with-matlab/_jcr_content/mainParsys/band/mainParsys/lockedsubnav/mainParsys/columns_548717802/d0a3d4aa-c0b6-412d-89e1-60f3f0c3d4df/image.adapt.full.medium.jpg/1731698195177.jpg)
Ray tracing with MATLAB.
Scattering MIMO
The Scattering MIMO Channel model in MATLAB is well suited for ISAC channels. The model simulates scenarios where signals emitted from a transmitting array reflect off multiple scatterers before reaching a receiving array. Importantly, the model incorporates the impact of moving scatterers, considered as targets, on the received signal, making it ideal for sensing applications. The channel accounts for various propagation effects including range-dependent time delay, gain, Doppler shift, phase change, and atmospheric losses due to gases, rain, fog, and clouds. These attenuation models are valid for frequencies ranging from 1 to 1,000 GHz, ensuring an accurate representation of signal degradation over a wide spectrum.
Hardware Limitations
As you transition from conceptual studies based on simulations to building hardware prototypes, it is crucial to consider the challenges and limitations associated with hardware. The high data rates required in an ISAC system will likely require you to implement parts of the design on an FPGA, which presents its own set of challenges.
First, developing signal processing algorithms on an FPGA is generally more complex than creating the equivalent designs in software. The availability of FPGA-ready IP blocks for tasks such as filtering, signal interpolation and decimation, and mathematical transforms will significantly ease this process compared to building everything from basic elements.
Second, to achieve the high data rates needed in ISAC, these IP blocks may need to handle multiple samples per clock cycle. DSP HDL Toolbox™ offers standardized signal processing IP blocks capable of processing multiple samples per clock cycle, enabling gigasample-per-second data rates.
Choosing the right hardware platform will depend on several factors. For localization and tracking, one important consideration is the required range resolution. For example, achieving a range resolution well below 1 meter will require a sampling rate in the order of hundreds of MHz, which imposes extreme demands on the hardware platform. One platform that meets such stringent specifications is the AMD RFSoC platform, which MATLAB natively supports. This means a Simulink® model using FPGA-ready IP blocks can be deployed to and run on this platform.
MATLAB also offers workflows for hardware impairment modeling:
MATLAB plays a pivotal role in advancing ISAC. By enabling users to simulate scenarios, design algorithms, and analyze data, MATLAB accelerates the development of integrated radar and communication systems. The MATLAB platform aids researchers in efficiently validating designs and optimizing connectivity, which is essential for applications like autonomous vehicles and smart cities. As ISAC becomes integral to future networks, MATLAB capabilities ensure that researchers can swiftly innovate and meet the demands of a highly interconnected world, driving the realization of the full potential of 6G.
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