Why Dynamic Multi-Band Selection is Critical for JCAS ?

In the evolving landscape of Joint Communication and Sensing (JCAS), the ability to flexibly choose between multiple frequency bands is not just a convenience, it’s a strategic enabler. From autonomous vehicles coordinating at dense urban intersections to UAVs conducting terrain mapping while maintaining data links, multi-band adaptability enables dynamic spectrum allocation across diverse operational scenarios. By intelligently selecting frequency bands based on mission context and environmental conditions, systems can optimize performance for communications, sensing, or both, depending on what the system needs most at any moment. 

 

The Power of Band Diversity 

Wireless bands each have their own characteristics. Lower frequencies like sub-6 GHz provide broader coverage and better penetration, while higher bands like mmWave or THz offer high bandwidth and fine spatial resolution. 

In JCAS systems, selecting the right set of bands for the task at hand enables systems to: 

• Increase communication throughput in real-time 

• Enhance sensing resolution for better environmental awareness 

• Balance trade-offs between data rate, localization precision, and robustness. 

 

But There’s a Catch: Coordination Complexity 

In a multi-band environment, different users or tasks may access separate bands—some dedicated to data transmission, others to object tracking. Without a well-defined coordination framework, this can lead to interference, inefficient resource use, and degraded system performance. Effective resource allocation in a multi-band JCAS system requires precise, real-time decisions, such as: 

• Which bands to assign for communication vs. sensing? 

• When to switch between bands or use them jointly? 

• How to distribute time, power, and bandwidth across the available spectrum? 

 There are three ways in which collaboration can occur across multiple bands [1]: 

 

Figure. 1 

Separate Roles for Separate Bands (Fig. 1a)

A straightforward approach assigns each band a dedicated role—for instance, using sub-6 GHz for communication and mmWave for sensing. Sub-6 GHz provides wide coverage, making it ideal for stable, long-range communication, while mmWave offers the high precision needed for accurate sensing. 

This separation simplifies integration with existing networks, especially those built primarily on sub-6 GHz infrastructure. However, it comes with trade-offs: the two functions remain siloed, and the full potential of the spectrum is underutilized. As a result, this method may not be the go-to solution for future integrated communication and sensing systems.

Multiple Bands Working Together (Fig. 1b)

A more advanced strategy involves carrier aggregation or multi-link techniques, where both sub-6 GHz and mmWave bands collaboratively handle either communication or sensing tasks. This approach significantly boosts performance by combining spectrum resources, resulting in higher data rates and better sensing resolution due to the expanded bandwidth. 

However, there’s a catch: different bands have different coverage characteristics, and integrating them requires more sophisticated hardware and software. Supporting technologies like precise power control and interference coordination becomes essential—raising both the complexity and power consumption of user devices.

Cross-Band Assistance for Enhanced Performance (Fig. 1c)

The most integrated approach involves bands assisting one another to complete a single task. For example, in a sensing scenario, a device might first use sub-6 GHz’s wider beams to scan a large area and identify the rough location of an object. Then, mmWave’s narrow beams can take over for detailed, high-resolution scanning. 

This cooperative method not only accelerates the sensing process but also enables data fusion between bands, improving overall sensing accuracy and performance. It’s a powerful demonstration of how strategic collaboration between bands can overcome the limitations of each when used in isolation. 

 

Figure. 2 

Consider a system [2], as in Fig. 2, where multiple transmitters (TX1, TX2, TX3) use a multiband transmission approach across a sparse and incoherent spectrum, such as in typical communication systems. Each transmitter emits distinct communication pilot signals on separate sub-bands (1, 2, 3), which reflect off nearby objects or people (close targets). A multiband receiver (RX), such as a mobile device or wearable, collects the incoming signals. If only single-band processing is used, closely spaced targets may become unresolvable due to limited resolution in that band. However, with multi-band processing, the receiver leverages the diversity across all sub-bands, allowing it to resolve multiple closely spaced targets (e.g., Target 1 and Target 2), improving range resolution and target differentiation. 

 

Why This Matters 

In real-world applications like vehicle-to-everything (V2X), smart factories, or drone-based inspection, the spectrum environment is dynamic. Congestion, fading, and blockage can affect certain bands more than others. Multi-band agility provides the resilience and flexibility needed to: 

• Maintain robust communication under interference 

• Perform precise sensing even in degraded environments 

• Jointly optimize performance across heterogeneous tasks 

Traditional single-band systems often fail to distinguish targets that are close together, leading to ambiguity, missed detections, or incorrect position estimates. Multi-band processing over a sparse and incoherent spectrum, enabled by existing communication infrastructure, enhances spatial resolution without requiring additional hardware, offering a cost-effective, scalable solution to these challenges.  

 

Looking Ahead 

As JCAS evolves toward more autonomous, distributed, and intelligent operation, effective multi-band spectrum management will become a foundational system capability. This requires: 

• Real-time optimization algorithms that adapt to changing environments 

• Cross-layer coordination between hardware, waveform, and scheduling 

• Spectrum awareness and predictive allocation 

 

Our ongoing work explores intelligent frameworks that can learn, adapt, and predict the best band usage strategies, bridging communication and sensing more efficiently than ever before. 

An article by Aymen Hamrouni.

References: 

[1] https://www.zte.com.cn/global/about/magazine/zte-technologies/2025/special-topic—5g-a-mmwave/expert-views-/multi-band-sensing–empowering-industry-applications.html 

[2] Jacopo Pegoraro, Jesus O. Lacruz, Michele Rossi, and Joerg Widmer. 2024. HiSAC: High-Resolution Sensing with Multiband Communication Signals. In Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems (SenSys ’24). Association for Computing Machinery, New York, NY, USA, 549–563. https://doi.org/10.1145/3666025.3699357