A Survey on STAR-RIS Enabled Joint Communications and Sensing: Fundamentals, Recent Advances and Research Challenges

1. Problem Statement

As wireless networks evolve toward sixth-generation (6G) systems, there is a growing demand for "perceptive" networks that can both transmit data and sense the physical environment. Traditionally, communication and sensing (radar) have been treated as disjoint systems, leading to redundant hardware, inefficient spectrum usage, and high operational complexity.

While Joint Communications and Sensing (JCAS)—also known as Integrated Sensing and Communication (ISAC)—aims to unify these functions using shared resources, it faces several fundamental hurdles:

• Inherent Trade-offs: Conflicting requirements between communication throughput and sensing accuracy (e.g., resolution vs. data rate).
• Propagation Limitations: Conventional Reconfigurable Intelligent Surfaces (RIS) only reflect signals, limiting coverage to a half-space (180°).
• Environmental Dynamics: Challenges in maintaining performance in high-mobility scenarios (UAVs, vehicles) and non-line-of-sight (NLOS) environments.
• Hardware/Design Constraints: Complexity in waveform design, beamforming, and real-time resource allocation.

2. Methodology

The paper employs a systematic and multi-dimensional survey methodology. Rather than just listing existing literature, the authors categorize the state-of-the-art research through several analytical lenses:

• Taxonomy of Fundamentals: They define the signal models, operating protocols (Energy Splitting, Mode Switching, and Time Switching), and hardware architectures (Passive vs. Active STAR-RIS).
• Multi-Level Classification: They classify JCAS systems based on integration levels (Coexistence, Cooperative, and Joint), waveform usage (Communication-centric, Sensing-centric, and Dual-function), and system architecture (Monostatic, Bistatic, and Multistatic).
• Comparative Analysis: The authors use comprehensive tables to compare different operating protocols, waveform types, security threat models, and optimization strategies.
• Critical Synthesis: The methodology involves identifying "lessons learned" from current research and mapping them against "open challenges" to propose a roadmap for future research.

3. Key Contributions

The paper provides several significant contributions to the field:

• Comprehensive Coverage of STAR-RIS for JCAS: Unlike previous surveys that focus on RIS or JCAS separately, this is one of the first to provide an in-depth, unified treatment of how Simultaneously Transmitting and Reflecting RIS (STAR-RIS) enables full-space (360°) JCAS.
• Detailed Technical Taxonomy: It provides a rigorous breakdown of:
  • Optimization Strategies: Reviewing methods like Semidefinite Relaxation (SDR), Successive Convex Approximation (SCA), and Alternating Optimization (AO).
  • Waveform Design: Analyzing the shift from traditional waveforms to learning-driven and pulsed-signal designs.
  • Advanced Access Schemes: Examining the integration of NOMA (Non-Orthogonal Multiple Access) and RSMA (Rate Splitting Multiple Access) to enhance connectivity.
• Security and Sustainability Analysis: It uniquely explores the intersection of JCAS with Physical Layer Security (PLS), anti-jamming, and Energy Efficiency (EE), including the role of Simultaneous Wireless Information and Power Transfer (SWIPT).
• Mobility and Aerial Integration: It reviews specialized architectures for high-mobility vehicular networks and UAV-assisted (Uncrewed Aerial Vehicle) communication/sensing.

4. Results and Findings

The survey synthesizes the current state of research to reach the following conclusions:

• STAR-RIS Superiority: STAR-RIS significantly expands the "feasible region" for JCAS by providing more degrees of freedom (DoFs) for beamforming and energy splitting compared to conventional reflective RIS.
• Optimization Trends: There is a clear transition from deterministic, perfect-CSI (Channel State Information) models toward robust, statistical, and learning-based (DRL/RL) models to handle real-world uncertainties.
• Complexity vs. Performance: While joint optimization (waveform + beamforming + surface coefficients) yields the highest performance, it introduces massive computational complexity that necessitates the development of low-complexity, scalable algorithms.
• Emerging Regimes: The research is moving toward near-field propagation (due to large-scale XL-arrays) and integrated sensing-communication-computation frameworks.

5. Significance

This paper serves as a foundational roadmap for researchers and practitioners in the 6G era. Its significance lies in:

• Bridging the Gap: It connects electromagnetic metasurface design with high-level network protocols and signal processing.
• Identifying Research Gaps: By highlighting unsolved problems—such as hardware-consistent modeling (amplitude-phase coupling), control signaling overhead, and the need for standardized benchmarking—it provides a clear direction for future academic and industrial investment.
• Enabling Perceptive Networks: It validates the potential of STAR-RIS as a key enabler for the "Internet of Everything" (IoE), where the network acts not just as a pipe for data, but as a sensor for the physical world.