Interference Management Techniques in Integrated Sensing and Communication Systems: A Comprehensive Survey

Core Concepts

Integrated sensing and communication systems require effective interference management techniques to address self-interference, mutual interference, clutter, and cross-link interference.

Abstract

The article discusses the demand for integrated sensing and communication systems in applications like autonomous driving and IoT services. It explores interference management techniques such as suppression, avoidance, and exploitation in ISAC systems. Various methods are reviewed including hardware optimization, analog cancellation algorithms, digital cancellation algorithms, multiple access techniques, beamforming design, and transmitted signal design. The content is structured into sections covering system model, single BS SI management, single BS MI management with downlink MI suppression and avoidance strategies.

Stats

Emerging AI services put forward the demand for dual functionalities of sensing and communication (S&C). Federal Communications Commission (FCC) proposed sharing 150 MHz of spectrum allocated for sensing applications in the 3.5 GHz band with communication applications. ISAC has been designated as one of the key scenarios for the sixth-generation (6G) mobile communication system by ITU in 2023.

Quotes

"ISAC supports ubiquitous communication and precise sensing throughout various applications." - Yangyang Niu et al. "Effective interference management can enhance system performance by refining channel modeling." - Yangyang Niu et al.

Key Insights Distilled From

Interference Management for Integrated Sensing and Communication Systems

by Yangyang Niu... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16189.pdf

Deeper Inquiries

How can ISAC systems adapt to dynamic environments where interference levels fluctuate?

In dynamic environments with fluctuating interference levels, ISAC systems can employ adaptive algorithms and techniques to mitigate the impact of changing interference. One approach is to use cognitive radio technology, which allows the system to dynamically adjust its parameters based on real-time interference conditions. By continuously monitoring the environment and adapting transmission strategies, such as adjusting power levels or switching frequency bands, ISAC systems can optimize performance in response to varying interference levels. Additionally, machine learning algorithms can be utilized for intelligent decision-making in adapting to dynamic environments. By training models on historical data and feedback from the system's performance under different interference scenarios, AI-powered algorithms can predict future interference patterns and proactively adjust system parameters for optimal operation. Furthermore, implementing advanced signal processing techniques like beamforming and spatial filtering can help in isolating desired signals from interfering sources. By focusing transmission beams towards intended targets or communication receivers while minimizing reception from unwanted directions, ISAC systems can effectively manage fluctuations in interference levels.

What are the potential drawbacks or limitations of using NOMA techniques in ISAC systems?

While NOMA (Non-Orthogonal Multiple Access) techniques offer significant advantages in enhancing resource utilization efficiency and managing inter-user interference within integrated sensing and communication (ISAC) systems, there are some potential drawbacks and limitations associated with their implementation: Complexity: Implementing NOMA requires sophisticated signal processing algorithms at both the transmitter and receiver ends. The complexity of coordinating multiple users' signals simultaneously may lead to increased computational overhead and hardware requirements. Interference Management: In NOMA-enabled ISAC systems, efficiently managing cross-link interferences between sensing signals and communication signals becomes challenging due to simultaneous transmissions over shared resources. Ensuring reliable detection of sensing targets while maintaining communication quality amidst overlapping transmissions poses a significant hurdle. Synchronization Issues: Achieving precise synchronization among multiple users sharing common resources is crucial for successful NOMA operation. Inaccurate timing alignment or channel estimation errors could result in degraded system performance due to inter-user interference. Power Allocation: Proper allocation of transmit power among different users is essential for maximizing spectral efficiency in NOMA-based ISAC setups. However, determining optimal power allocations that balance trade-offs between user fairness, energy efficiency, and overall system capacity requires complex optimization schemes. Hardware Constraints: The hardware constraints imposed by existing infrastructure may limit the full realization of NOMA benefits within ISAC systems. Compatibility issues with legacy equipment or restrictions on antenna configurations could hinder seamless integration of NOMA functionalities into conventional networks.

How can advancements in AI impact the future development of integrated sensing and communication technologies?

Advancements in artificial intelligence (AI) have profound implications for shaping the future development of integrated sensing and communication technologies: 1. Enhanced Interference Management: AI-powered algorithms enable more efficient handling of various types of interferences encountered in integrated sensing and communication (ISAC) systems - from self-interference suppression to mutual-interference avoidance strategies through intelligent beamforming solutions. 2. Dynamic Resource Allocation: AI-driven approaches facilitate adaptive resource allocation based on real-time environmental conditions such as changing interference patterns or network congestion situations. 3. Predictive Maintenance: Machine learning models applied within ISAC frameworks allow predictive maintenance capabilities by analyzing sensor data trends indicative of potential failures or anomalies before they occur. 4. Cognitive Radio Capabilities: With AI integration, cognitive radio functionalities become more robust as they learn from past experiences to autonomously optimize spectrum usage while mitigating harmful co-channel interferences. 5. Optimized Signal Processing: Advanced AI algorithms enhance signal processing tasks like noise reduction filters or echo cancellation methods by intelligently identifying patterns within noisy data streams. 6. Autonomous Operation: Through reinforcement learning mechanisms combined with sensor fusion capabilities enabled by AI technologies; autonomous decision-making processes improve operational efficiencies without human intervention. 7. Security Enhancements: Utilizing machine learning for anomaly detection helps identify unauthorized access attempts or security breaches within an ISCA ecosystem safeguarding sensitive information transmitted across networks. These advancements collectively pave the way for smarter, more efficient integrated sensing & communications ecosystems capable of meeting evolving demands across diverse applications ranging from smart cities & IoT services to autonomous vehicles & industrial automation sectors.