Space-Time RSMA: Robust Multi-Access

• ST-RSMA is a wireless access approach that integrates RSMA with space–time coding to achieve full diversity and robust performance under CSI uncertainties.
• It employs Alamouti-type encoding and WMMSE-based precoder optimization to enhance spectral efficiency and fairness in high-mobility and dense network scenarios.
• ST-RSMA is applicable in LEO satellite, ISAC, and 6G networks, enabling joint communication and sensing through improved interference management.

Space-Time Rate-Splitting Multiple Access (ST-RSMA) is a development in multi-user wireless access that fuses the interference management capabilities of Rate-Splitting Multiple Access (RSMA) with space-time coding, yielding a transmission paradigm that achieves enhanced diversity, robustness, and spectral efficiency. The fundamental innovation is the integration of space–time coding into the transmission of the common stream component, providing full diversity gains independently of channel state information (CSI) uncertainty and user/network load. This section situates ST-RSMA within the context of current research, with specific technical and application details drawn from multibeam LEO satellite networks (Seong et al., 20 Oct 2025), integrated sensing and communication (ISAC) systems (Chen et al., 2023), and high-throughput wireless environments.

1. RSMA Fundamentals and Extensions to Space-Time Domains

RSMA divides each user's message into a “common” part (intended for multiple users) and a “private” part (intended for the individual user). The transmitter linearly precodes and superposes the common stream and private streams. By partially decoding interference (common stream) and partially treating interference as noise (private streams), RSMA flexibly adapts to diverse channel disparities and imperfect CSI (Mao et al., 2018, Aditya et al., 13 Feb 2025).

Space-Time RSMA (“ST-RSMA,” Editor's term) extends this framework via space–time coding of the common stream. A canonical example is the Alamouti-type encoding:

$$s_c^{(1)} = [ s_c^{(1)}, s_c^{(2)} ]^\top, \quad s_c^{(2)} = [ - (s_c^{(2)})^*, (s_c^{(1)})^* ]^\top$$

By distributing the common stream in the space–time domain, ST-RSMA achieves full diversity, mitigating the limitations imposed by using a single beamforming vector in traditional RSMA.

2. Signal Model, Space-Time Coding, and Diversity

In ST-RSMA, the transmitted signal at symbol period $t$ can be expressed as

$$x[t] = P_c[t] S_c[t] + \sum_{k=1}^K p_k[t] s_{p,k}[t]$$

where $P_c[t]$ operates as a space–time encoding matrix for the common stream across antennas and time slots, and $\{p_k[t]\}$ are the beamforming vectors for the private streams.

By integrating space–time coding into $S_c[t]$, the system no longer requires the design of a dedicated common beamformer for all users. Instead, all users receive the common stream with full space–time diversity, regardless of instantaneous channel variations, CSI accuracy, or user count. Numerical results in multibeam LEO satellite scenarios demonstrate that the diversity gain in the common stream transmission ensures robust decoding performance under severe CSI uncertainties and rapidly changing network loads (Seong et al., 20 Oct 2025).

3. WMMSE-Based Precoder and Power Allocation Optimization

ST-RSMA adapts the weighted minimum mean square error (WMMSE) framework for joint optimization of the power allocation, space–time code parameters for the common stream, and beamforming vectors for private streams. The algorithm’s structure is:

• Compute the mean square errors (MSEs) for each stream (common and private).
• Formulate WMSEs: $\xi_{c,k}$ and $\xi_{p,k}$ for user $k$, with explicit correspondence to achievable rates (e.g., $$1 + \log_2(\epsilon_{c,k}^\text{MMSE}) = 1 - R_{c,k}$$).
• Iteratively update equalizers and weights (derived from MMSE conditions); update power allocation and beamforming variables via convex sub-problems (e.g., using interior-point methods).
• The objective in the representative LEO SATCOM application is to maximize the minimum user spectral efficiency (max–min fairness):

$$\max_{\{P_c, p_k\}} \min_{k} \left\{ R_{c,k} + R_{p,k} \right\}$$

Simulations confirm superior robustness to CSI errors and scalability to large user counts, with improvements in minimum spectral efficiency up to 44% over conventional RSMA as the number of users increases (Seong et al., 20 Oct 2025).

4. Interference Management and System Robustness

ST-RSMA extends the core interference management advantages of RSMA to space–time coded systems:

• The common stream, distributed via space–time codes, enables all users to decode part of the interference with diversity protection.
• Private streams are beamformed separately, exploiting available spatial degrees–of–freedom.
• In multibeam satellite networks, where high mobility (e.g., 7.56 km/s for LEO satellites) causes outdated CSI and severe inter-beam interference, ST-RSMA’s common stream diversity offers robustness unmatched by conventional single-beamforming RSMA, SDMA, or NOMA.
• In ISAC systems, the joint waveform (common and private streams) can be optimized to simultaneously meet communication max-min fairness targets and minimize the largest eigenvalue of the Cramér-Rao bound (CRB) for multi-target radar estimation (Chen et al., 2023).

5. Performance Comparisons and Scalability

Extensive simulations reveal that ST-RSMA consistently outperforms conventional RSMA, SDMA, NOMA, and multicast in both user fairness and aggregate rate metrics:

Technique	CSI Error Robustness	Minimum Spectral Efficiency	Scalability (User Count)
SDMA	Poor	Low	Poor
NOMA	Moderate	Worse at low channel disparity	Moderate
RSMA (Conv.)	Moderate	Moderate	Moderate
ST-RSMA	High	High (up to 44% gain)	Excellent

For example, as the standard deviation of the CSI error ($\sigma_e$) increases, ST-RSMA maintains a widening margin over RSMA; at $\sigma_e = 2$, a 39% improvement is reported (Seong et al., 20 Oct 2025). When the user count grows, the gap in minimum spectral efficiency further expands, highlighting the scalability properties essential for massive connectivity in satellite networks.

6. Applications and Implications for 6G and Beyond

ST-RSMA is specifically positioned for advanced wireless applications:

• Multibeam LEO satellite networks: ST-RSMA elegantly adapts to the nonstationary environments encountered in low Earth orbit, where rapid channel variations and high network loads are the norm.
• Joint communications and radar (RadCom, ISAC): By optimizing the joint waveform, ST-RSMA supports both high-throughput communications and accurate multi-target radar detection, with enhanced trade-off regions over SDMA (Chen et al., 2023, Aditya et al., 13 Feb 2025).
• High-throughput and low-latency terrestrial networks: The diversity gains in the common stream transmission directly benefit reliability and fairness in dense deployments, overloaded scenarios, and under CSI uncertainty (Seong et al., 20 Oct 2025, Aditya et al., 13 Feb 2025).

A plausible implication is that ST-RSMA provides a foundation for the next generation of multiple access schemes in 6G and non-terrestrial networks, capable of flexibly adapting to diverse topologies, dynamic channel conditions, and integrated sensing requirements.

7. Future Directions

Current research points toward several extensions:

• Adaptation for hardware non-idealities and practical deployment constraints in on-orbit LEO satellites.
• Extension to other domains, e.g., space–frequency rate-splitting for wideband systems.
• Integration with advanced receiver architectures (non-SIC, low-complexity decoders) for further reduction in complexity and enhancement of robustness.
• Standardization activities in 3GPP and ETSI are ongoing, with RSMA and its space–time variants being considered for 6G air interfaces (Aditya et al., 13 Feb 2025).

A plausible implication is that as hardware, network, and protocol advances continue, ST-RSMA will be adapted for real-world deployment, building on the mathematical, algorithmic, and prototyping demonstrations in recent literature.

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In summary, ST-RSMA leverages space–time coding to provide full diversity in the common stream, overcoming CSI uncertainty and network load limitations of conventional RSMA. The framework’s integration with WMMSE precoder optimization yields robust, scalable, and high-efficiency performance in satellite, ISAC, and broadband wireless networks, and is under active consideration for 6G standardization and future integrated communication-sensing paradigms.