Heterogeneous Satellite Clusters

• Heterogeneous Satellite Clusters are federated groups of satellites with diverse bus architectures, payloads, and communication protocols that jointly fulfill mission objectives.
• They enhance coverage, revisit rates, and fault tolerance by enabling dynamic resource sharing and autonomous coordination across multi-tier constellations.
• Analytical frameworks using stochastic geometry, federated learning, and MARL optimize resource allocation, mitigate interference, and manage operational constraints.

A heterogeneous satellite cluster is a federated grouping of spacecraft—typically small satellites (CubeSats, LEO platforms) or multi-tier constellations—distinct in their bus architectures, orbital parameters, sensor payloads, communication protocols, and operator policies, yet jointly orchestrated to deliver aggregate service, resource sharing, or distributed intelligence objectives. Heterogeneity arises from diverse ownership, independent subsystem design, or cross-provider collaborations. These federated systems are central topics in recent literature under the umbrellas of Distributed Space Systems (DSS), Federated Satellite Systems (FSS), Internet of Satellites (IoSat), and heterogeneous satellite networks (HetSatNets) (Batista et al., 2022, Hartmann et al., 13 Aug 2024, Li et al., 15 May 2025).

1. Taxonomy and Sources of Heterogeneity

Heterogeneity in satellite clusters spans multiple system layers:

• Bus and Platform Variation: Platforms may differ in form factor (e.g., 1U/3U/6U CubeSats), power budget, attitude control, and onboard processing (Batista et al., 2022).
• Payload Diversity: Optical imagers (VIS/NIR), SAR instruments, RF collectors, and environmental sensors introduce hardware, data modality, and processing differences (Hady et al., 16 Nov 2025).
• Communication Links: UHF/S-band/X-band radios, software-defined radios (SDRs), and various inter-satellite link (ISL) protocols (RF or optical) generate link asymmetries.
• Ground Segment and Operations: Heterogeneous ground-station networks differ in antenna capabilities, data handling, policy enforcement, and scheduling rules.
• Data and Feature Disparity: Onboard data distributions ($\mathbb{P}_k$), feature spaces, and model architectures often diverge, particularly with federated learning use cases (Hartmann et al., 13 Aug 2024).
• Ownership and Autonomy: Nodes may be operated by independent entities, each with distinct mission objectives, security policy, and control authority (Batista et al., 2022).

In multi-tier HetSatNets, “tier” refers to orbital altitude class, beam pattern, or service capability, further differentiating satellites by slant range, antenna characteristics, and transmission power (Li et al., 15 May 2025).

2. System Capabilities and Functionalities

Heterogeneous satellite clusters exhibit several advantages over homogeneous swarms:

• Enhanced Coverage & Revisit: Diverse orbits and field-of-view (FOV) geometries aggregate to higher region-of-interest (ROI) coverage and improved revisit rates (Batista et al., 2022, Choi et al., 2023).
• Resource Sharing: Under the satellites-as-a-service (SaS) paradigm, excess bandwidth or idle processing can be dynamically reallocated, and third-party experiments hosted opportunistically (Batista et al., 2022).
• Fault Tolerance and Resilience: Redundancy in payload types and orbital diversity mitigates single-point failures; e.g., if optical imaging is cloud-blocked, SAR can compensate (Hady et al., 16 Nov 2025).
• Dynamic Federation: Participants can join or leave without constellation reconfiguration; adaptability is quantified by the “retrofitting capability” metric—re-solving time of the system-level optimization (Batista et al., 2022).
• Autonomous Coordination: Multi-agent reinforcement learning (MARL) and hierarchical FL pipelines enable decentralized, role-aware coordination across agents with asymmetric observability and control policies (Hady et al., 16 Nov 2025, Liu et al., 30 Jul 2025).

3. Analytical and Optimization Frameworks

A. Constraint Satisfaction and Resource Modeling

At the system level, coverage, latency, and resource utilization are key figures of merit. Decision variables $x_{i,j}(t)$ indicate if satellite $i$ serves ROI $j$ at time $t$, subject to:

$$\begin{aligned} &x_{i,j}(t) \le 1_{i,j}(t) \ &\sum_{j} x_{i,j}(t) D_j \le S_i(t) \ &\sum_{j} x_{i,j}(t) D_j \le B_i \Delta t \ &\sum_{\text{tasks}} P_{\text{task}} \le P_i(t) \end{aligned}$$

where $S_i(t)$ and $B_i$ are storage and bandwidth, and $P_i(t)$ is the power budget (Batista et al., 2022). Objective functions target maximizing joint coverage ($\prod_j P_{\text{cov},j}$) or minimizing latency.

B. Stochastic Geometric Models

LEO networks with multi-altitude, multi-tier constellations are frequently modeled as Cox point processes, with nested Poisson orbit and satellite processes (Choi et al., 2023, Li et al., 15 May 2025). Parameterizations account for altitude band, satellite density, antenna beam patterns, and user association behavior (nearest or max-SINR). These abstractions yield tractable expressions for metrics such as:

• Nearest-satellite distance CDF,
• Outage probability under generalized fading,
• Aggregated interference (Laplace transform).

Weighted metrics combining coverage probability (CP), non-handover probability (NHP), and delay outage probability (DOP) support holistic trade-off analysis (Li et al., 15 May 2025).

C. Federated Learning and Reinforcement Learning

Heterogeneous federated learning (FL) aggregates local losses $F_k(w)$ with weights reflecting data size, quality, and staleness:

$$F(w) = \sum_{k=1}^K p_k F_k(w),\quad p_k = \frac{n_k}{\sum_\ell n_\ell}$$

Hierarchical frameworks such as SFedSat introduce two-stage clustering (parameter servers, ground stations), data similarity and closeness, and semi-supervised loss blending supervised ground-station updates with pseudo-label-driven satellite training (Liu et al., 30 Jul 2025).

MARL formulations model each satellite as an agent with individualized action and observation spaces; HAPPO and HATRPO methods decompose the value function per agent for stability and heterogeneity adaptation (Hady et al., 16 Nov 2025).

4. Operational and Physical Constraints

Major operational constraints include:

• Power: Onboard power $P_i(t)$ limits payload activity, communication, and attitude maneuvers.
• Storage and Memory: Data buffering $S_i(t)$ is finite—mission planning must allocate downlink windows and ISL relays for overflow mitigation.
• Bandwidth and Link Budget: Communication constraints $B_i$ depend on radio bands, ISL topology, ground-station geometry, and protocol stack (standard vs proprietary).
• Protocol Interoperability: Differences in coding, handshake, and link-layer operations challenge seamless integration.
• Scheduling and Autonomy: Authority and access policies restrict tasking flexibility (e.g., minimum revisit constraints, refusal of certain command types).
• Partial Observability: In EO scenarios, cloud cover, agent state, and ground-station access may only be locally known, with partial information sharing imposed by hardware/software heterogeneity (Hady et al., 16 Nov 2025).
• Handover and Mobility: User association policies (nearest vs max-SINR) and satellite angular velocity determine handover rates and session persistence (Li et al., 15 May 2025).

5. Design Strategies and Mitigation Techniques

Several mitigation strategies have emerged to address heterogeneity:

• Weighted Aggregation and Trust Scores: FL methods assign aggregation weights based on local data quality, staleness, or trust (Hartmann et al., 13 Aug 2024).
• Personalization and Clustering: Grouping of similar nodes (e.g., sensors, orbits) or model interpolation yields cluster-specific aggregates or personalized models.
• Adaptive Compression: Communication overhead is reduced via sparsification and adaptive quantization of model updates, with bit-width tuned to gradient volatility (Liu et al., 30 Jul 2025).
• Staleness-aware Scheduling: Asynchronous and semi-synchronous protocols (e.g., FedSpace, semi-async clustering) align aggregation to orbital windows, reducing idle time and bounding staleness (Hartmann et al., 13 Aug 2024, Liu et al., 30 Jul 2025).
• MARL Specialization: Per-agent policy updates and centralized training (CTDE) permit emergent task allocation, with SAR and optical satellite agents specializing in their respective observation regimes (Hady et al., 16 Nov 2025).
• Multi-objective Optimization: Weighted metrics and Pareto frontier methods enable system-level trade-off navigation (coverage, latency, connection stability) (Li et al., 15 May 2025).

6. Performance Metrics and Empirical Findings

Tables and case studies in the literature quantify the impact of heterogeneity and mitigation approaches:

Framework	Key Metric	Impact/Result
SFedSat	Processing time	3× speedup vs. FedAvg, same accuracy (Liu et al., 30 Jul 2025)
SFedSat	Energy consumption	4× reduction, quantization yields 5–7× model shrink
FedSpace	Convergence rounds	50 (semi-async) vs. 80 (sync) (Hartmann et al., 13 Aug 2024)
MAPPO	Resource optimization	+30% return gain (hard cases) (Hady et al., 16 Nov 2025)

Fixed-parameter studies demonstrate nontrivial trade-offs: denser low-altitude or multi-tier deployments improve coverage and reduce nearest-neighbor distance but can elevate interference, lowering coverage probability after a threshold density (Choi et al., 2023, Li et al., 15 May 2025). Max-SINR association outperforms nearest-satellite in coverage and handover metrics, provided upper-tier satellites have sufficient transmit power (Li et al., 15 May 2025). Empirical FL results show that hierarchical, clustered, and communication-efficient schemes are essential for scaling to thousands of heterogeneous nodes (Liu et al., 30 Jul 2025).

7. Open Directions and Practical Considerations

Emerging research focuses on:

• Hierarchical MARL and FL for cross-cluster federation with communication-efficient and credit-assignment-aware protocols (Hady et al., 16 Nov 2025, Liu et al., 30 Jul 2025).
• Cross-heterogeneity co-design: simultaneous optimization of data, device, and feature heterogeneity under unified protocols (Hartmann et al., 13 Aug 2024).
• Multi-objective and preference-aware optimization, leveraging weighted or Pareto-efficient formulations to accommodate operator and mission differences (Hartmann et al., 13 Aug 2024, Li et al., 15 May 2025).
• Protocol standardization: lightweight and versioned model formats for model and update exchange.
• On-orbit experimentation: validation of decentralized, learning-enabled resource management stacks on mixed CubeSat/hybrid swarms (Hartmann et al., 13 Aug 2024).
• Dynamic association and user-centric scheduling: stochastic geometric models extended to accommodate temporal mobility, orbital maneuvers, and variable demand (Li et al., 15 May 2025).

These directions are critical for the scalable, robust deployment of heterogeneous satellite clusters in both commercial and scientific non-terrestrial networks.