# Autonomous Max-Flow Interplanetary Laser Link Scheduling for Martian Exploration

Jason Gerard, Andre Ibrahim, Juan A Fraire, Sandra Céspedes

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Jason Gerard, Andre Ibrahim, Juan A Fraire, Sandra Céspedes. Autonomous Max-Flow Interplanetary Laser Link Scheduling for Martian Exploration. 2024 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), Dec 2024, Daytona Beach, United States. pp.70-75, 10.1109/WiSEE61249.2024.10850428. hal-05357375

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Distributed under a Creative Commons Attribution 4.0 International License
Autonomous Max-Flow Interplanetary Laser Link
Scheduling for Martian Exploration

Jason Gerard*, Andre Ibrahim*, Juan A. Fraire‡, Sandra Céspedes*

*Department of Computer Science & Software Engineering, Concordia University, Montreal, Canada.

†Inria, INSA Lyon, CITI, UR3720, 69621 Villeurbanne, France.

‡CONICET - Universidad Nacional de Córdoba, Córdoba, Argentina.

*Abstract*—New communication technologies like delay and disruption-tolerant networking (DTN) and free-space laser communications are being developed to handle massive planetary telemetry data. However, existing infrastructure cannot meet the scalability and autonomy demands of future space missions, resulting in 95% of data left on the mission planet. Manual scheduling of deep-space laser transmissions by operators is inefficient and error-prone, limiting mission parallelization, while autonomous scheduling, though promising, may limit optimal network routes reducing the network capacity. We hypothesize that autonomously scheduling contact opportunities will enable acquisition, tracking, and pointing (ATP) for free-space lasers and increase the capacity of interplanetary backhaul networks. In this work, we propose the Laser Link Scheduler (LLS), which combines a novel max-flow network capacity model and a decision-making algorithm for contact selection. We define a series of deep-space communication scenarios where data is collected from orbiters around Mars and backhauled to Earth. LLS is evaluated in simulations using contact plans generated based on realistic conditions and orbital dynamics. Our results confirm that autonomous contact scheduling increases network capacity by 1.5-2x, reducing operational costs and enabling highly parallel space exploration missions.

# I. INTRODUCTION

Reusable rockets and the CubeSat standard have decreased space flight and payload launch costs, enabling massive concurrent exploration missions. These involve rovers, landers, probes, and low-cost wireless sensors, each producing large amounts of data that must be transmitted back to Earth for analysis [2]. However, the existing network infrastructure, centered around NASA's deep space network (DSN), cannot meet the missions' bandwidth requirements. The lack of network autonomy, requiring manual effort from mission operators, paired with the low network capacity of the DSN, results in 95% of mission data being left on the source device [3]. With new missions and programs like LunaNet, NASA's Artemis program, SpaceX's Mars program, and NASA's Mars Exploration Program, the demand for deep space communication systems will only increase [4].

To further scale interplanetary networking (IPN), satellites orbiting the Sun at Lagrange points, planets, and moons can be used to transmit data across interplanetary distances. This increases channel capacity by leveraging extended visibility periods and reduces the energy consumption of resource-constrained devices on the ground. These satellites use the

delay and disruption tolerant networking (DTN) paradigm adapted to space environments. DTN protocols account for long propagation distances between planets and disruptions due to planetary occlusions and orbital dynamics. Key components of DTN are the Bundle Protocol (BP) and convergence layer adapters, which provide DTN services while enabling interoperability between DTN and terrestrial protocols.

Compared to traditional radio frequency (RF) communication, inter-satellite links (ISL), using free-space optical (FSO) technology, provide high data rates from Gbps to Tbps and are employed in IPN to increase network capacity significantly [5]. The superior size, weight, and power (SWaP) tradeoff of laser systems make them particularly advantageous for space missions where these factors are critical constraints. However, the use of lasers also presents challenges, primarily due to their smaller beam width, which necessitates the use of acquisition, tracking, and pointing (ATP) systems to establish a stable communication link before data transmission can occur [6]. The most common ATP process, primarily used for intra-constellation ISLs, uses a bright beacon signal from the receiving node to guide the transmitter's alignment [7]. While effective for shorter distances, this method is limited when used in deep space because the round-trip light time exceeds the contact window's duration. NASA's Jet Propulsion Laboratory (JPL) developed a star tracker-based ATP method that can provide accuracy and stability without needing a beacon [8]. Static ISLs are effective for intra-constellation communication as they can be permanently aligned but are energy-intensive. In contrast, for DTN deep space missions with strict SWaP requirements, dynamic ISLs are necessary, requiring ATP systems to establish links on demand [9].

ATP requires the transmitting laser to be pointed directly at the receiving telescope to establish the link. To maintain the one-transmitter-to-one-receiver invariant, ATP must be synchronized. For near-Earth networks, the ground stations can synchronize ATP in real-time, but due to long propagation delays in deep space networks, the synchronization between satellites for ATP must be done a priori. Figure 1-part 2a, shows the two files that satellites use for ATP: i) the *contact plan* (CP), a time-varying model of a network, formatted as a list of fields that describe the duration (start and end time) and data rate for each contact opportunity between a pair of nodes; and ii) the *azimuth, elevation, range report* (AER), which provides the horizontal direction (azimuth), vertical angle (el-
<figure><img src="image_2.png" /><figcaption>Fig. 1: Framework for ATP, routing, and forwarding in scheduled deep space DTNs. 1) Orbital propagators and communication models compute the contact plan (CP). Next, the CP is post-processed with a scheduling algorithm, such as the Laser Link Scheduler (LLS) proposed herein. Mission-critical and delay-intolerant data can then be added manually to the contact plan before it is distributed to the DTN nodes. 2.a) Acquisition, tracking, and pointing (ATP) for free-space laser link establishment uses the scheduled contact plan, azimuth, elevation, range report, and the satellites onboard star trackers. 2.b) The route table is computed using a DTN routing algorithm, optionally using the contact plan. Nodes query the route table for the outbound queue used to forward the bundle. Figure adapted from [1].</figcaption></figure>

evaluation), and distance (range) between two objects for precise spatial orientation and tracking for each access interval. These inputs are critical for automating ATP in deep space, which currently depends on operators at the mission operation centers (MOCs) manually scheduling satellite contacts. The manual process is prone to errors, has high operational overhead, and limits the network's ability to scale for concurrent missions due to its lack of autonomy [3]. An automated solution can optimize optical ISL selection, maximizing network capacity in deep-space DTNs. It does this through autonomous contact plan-based scheduling while allowing mission-critical contacts to be manually scheduled.

Figure 1-part 2b, shows the scheduled contact plan can be natively used at the network layer for DTN routing and forwarding. A popular algorithm, Contact Graph Routing (CGR), is used by NASA's ION implementation of BP [1].

Lasers offer higher data rates than RF, making them ideal for transmitting high volumes of mission data over deep space links. However, the laser's smaller beam spread necessitates precise mechanical alignment between the transmitting laser and the receiving telescope. This is achieved through ATP, which requires a synchronized contact schedule between satellites. The schedule also limits the search space of the routing algorithm, possibly eliminating optimal routes and reducing network capacity. We hypothesize that a scheduling algorithm based on a maximum-flow network capacity model can enable ATP for free-space lasers while increasing the scheduled capacity of interplanetary backhaul networks.

## A. Related Work

One of the first works for contact scheduling in DTNs aims to maximize the fairness of the network by modeling the duration each contact is disabled for [10]. The authors present the contact plan design problem, which optimizes contact plans for fairness and network capacity from an initial contact topology. The problem is first formalized as a mixed

integer linear programming (MILP) model. However, this is shown to become computationally intractable as network sizes increase, leading to the development of a scalable polynomial time-bounded algorithm called the Fair Contact Plan (FCP). FCP uses the Blossom algorithm over a time-expanded graph to compute the contact plan. The scheduled contact plans are then evaluated in a discrete-event Monte Carlo simulation, using BP, to calculate the network capacity, max-min fairness index, and Jain's fairness index. The evaluation and case study do not consider interplanetary communication or long propagation delays. This work is extended with the Routing-Aware Contact Plan algorithm [11]. The initial fair contact plan $L'$ is improved through a post-processing step based on the global view of the scheduled contacts. The authors apply three search algorithms: first improvement, steepest descent, and simulated annealing. The delivery delay is further minimized through the Traffic-Aware Contact Plan algorithm, a MILP model that reduces delivery time by 10% and system resource usage by 42%, compared to existing models [12]. Due to the model's complexity, the authors recommend exploring heuristic scheduling approaches for future research to scale to more extensive networks.

The authors in [13] present a battery-aware approach to optimizing inter-satellite communication by integrating a linear battery model (LiBaM) into a MILP framework, which determines link feasibility based on energy consumption and generation. To refine the contact plan, a more complex and accurate Kinetic Battery Model (KiBaM) is used in post-processing. This model captures non-linear battery behaviors like recovery and rate-capacity effects. The evaluation shows a 10% battery margin above the minimum 50% capacity effectively balances the differences between the LiBaM and KiBaM models. Validation using the Ulloriaq space mission scenario with GOMX-4 satellites demonstrates that this method reduces battery depletion risk to near zero compared
<figure><img src="image_3.png" /><figcaption>Fig. 2: Topology of Mars-Earth backhaul network</figcaption></figure>

to battery-agnostic techniques.

The authors in [14] show that computing the maximum flow of a network is super-exponential without any reductions in problem definition or topology pruning. They propose Max-Flow-Based Routing (MFR), which prunes the topology into a trivial network and then computes the maximum flow for the partial network. They prove that this will still result in an optimal solution. Although MFR uses precomputed information for its routing and forwarding decisions, it does not meet the requirements of ATP. This is because the resulting topology transmitted to the satellites for real-time bundle-level routing and forwarding does not synchronize the contact opportunities between nodes. Instead, the authors focus on optimizing data flow, where traditional methods like CGR fall short due to the increasing complexity of larger networks.

The Multi-Attribute Routing and Scheduling (MARS) algorithm, introduced in [15], uses PROMETHEE II: a multi-attribute decision-making (MADM) technique. To schedule bundles, MARS uses four main attributes: size, priority, time to live, and time in the buffer. Like MFR, MARS schedules bundles but not contact opportunities, so it does not maintain the synchronization invariant required for ATP.

## B. Contributions

To the best of our knowledge, this is the first work to perform interplanetary contact scheduling, including delays and disruptions, for free-space laser communications. This work contributes a novel flow-based backhaul network capacity model paired with an autonomous contact opportunity scheduling algorithm. The components maximize network capacity while enabling free-space laser ATP by synchronizing contacts between DTN nodes.

# II. LASER LINK SCHEDULER (LLS)

## A. System Model

The target scenario for this work is a deep space satellite backhaul network composed of orbiters and relays, similar to the topology illustrated in Fig. 2. In this scenario, Mars's orbiters generate scientific data destined for Earth. The relay satellites orbiting Mars, capable of establishing interplanetary links, are repurposed scientific satellites whose missions have expired. Each satellite is equipped with a single free-space

laser, they can only establish one link with another satellite at a time [16]. Data in the backhaul network flows in a single direction, from the Mars orbiters which transmit data to a Mars relay, which can then forward data to an Earth relay.

The scenario contains common space mission data: telemetry, tracking, and command (TT&C). The majority of network capacity is scientific telemetry data generated by the Mars orbiters. Figure 1 shows the standard operating procedure to distribute contact plans from the MOC to the satellites as command data. The contact plan is generated at the MOC and then scheduled by restricting contact opportunities. This synchronizes the satellites in the network, enabling ATP. Mission operators can manually schedule command data after the capacity-optimized schedule is created.

## B. LLS operation

The scheduling algorithm is composed of three different parts: i) the time-expanded graph, a data structure used to model the time-varying topology of the network, ii) our novel interplanetary backhaul capacity model, used to compute the weights of the edges, and iii) the Blossom algorithm, used to calculate the max-weight maximal matching based on the capacity model.

The pseudocode implementation of the scheduler is defined in Alg. 1, which takes a contact plan in ION format as input and breaks it down into $K \times N \times N$ discrete decision points, each representing a binary decision to allocate the edge between nodes i and j for state k with a duration of $T_k$. The algorithm iterates through each decision point in the contact topology $P$, modeled by a time-expanded graph, on lines 2 and 3. On line 4, the delta capacity for each edge, $\Delta C_{k,i,j}$, is computed using a dynamic programming approach. The capacities of the previous k states with selected contacts are memoized, allowing quick computation of the network capacity for each binary decision point. The algorithm can use $\Delta C_{k,i,j}$ to assess the change in available network capacity if the edge of $P_{k,i,j}$ is selected for the scheduled contact plan $L$. The delta capacity is then used on line 5 in a weighted sum with $DCT_{i,j}$ to compute the weight for that edge in state $k$. The disabled contact time for an edge, $DCT_{i,j}$, is the sum of the duration of the states in the contact plan where the edge is not selected. The weighted sum on line 5 uses the term $\alpha$ to adjust how important fairness is concerning the network capacity. For this work, $\alpha = 0.1$ is used as a tiebreaker so that when two or more edges result in the same increase in network capacity the edge used the least will be chosen. On line 6, a max-weight maximal matching is performed for state $k$. It uses the Blossom algorithm with the contact topology and the edge weights matrix as inputs. On line 7, the DCT matrix is updated for all edges that were not selected. The output of this algorithm, on line 8, defined as the scheduled contact plan $L$, is utilized by the physical layer for ATP and at the network layer for routing and forwarding decisions. An open-source implementation of LLS, implemented in Python using
the NumPy and NetworkX libraries, is publically available<fnref n="1" />.

**Algorithm 1** Laser Link Scheduler Algorithm

**Input:** Contact topology *P*, size (*K*, *N*, *N*)

State durations *T*, size *K*

IPN node to planet map *X*

**Output:** Contact Plan *L*, size (*K*, *N*, *N*)

1: $DCT_{i,j} \leftarrow 0 \quad \forall i, j$

2: **for** *k* $\leftarrow 0, K$ **do**

3: **for** *i*, *j* $\leftarrow 0, N$ **do**

4: $\Delta C_{k,i,j} \leftarrow capacity(P_k, L, X)$

5: $W_{k,i,j} \leftarrow (1 - \alpha) \cdot \Delta C_{k,i,j} + \alpha \cdot DCT_{i,j}$

6: $L_k \leftarrow Blossom(P_k, W_k)$

7: $DCT_{i,j} \leftarrow DCT_{i,j} + L_{k,i,j} \cdot T_k \quad \forall i, j$

8: **return** *L*

The max-flow network capacity model, used in Alg. 1-line 4, is designed to leverage the assumption that space missions backhaul all data to Earth for analysis. We can use this to understand the directionality of the network and the intended flow of data. The max-flow min-cut theorem is designed over a static topology but can be expanded to temporal flows over a time-expanded graph of polynomial size [17]. The maximum temporal flow equals the minimum temporal cut, from source to destination, by decomposing the temporal flows into flows each over a single s-t temporal path. The theorem, in the case of unbounded node buffers, serves as the basis for our interplanetary backhaul capacity model, where we can first compute the flow capacity of any given edge between nodes *i* and *j* for the entire duration of the network across all *K* states, defined by

$$flow_{i,j} = \sum_{k=1}^{K} L_{k,i,j} \cdot T_{transmission} \cdot bit\_rate, \quad (1)$$

where $L_{k,i,j}$ is a binary value set to 1 if the edge was selected or 0 if it was not. $d_{transmission}$ is the duration of the transmission, defined as $T_k - ATP_{k,i,j}$, the difference between the total state duration and the time it takes to perform ATP. The bit rate of the free-space laser, *bit_rate*, is defined as $min(R_i, R_j)$, where *R* is the set of bit rates for each laser.

By recognizing that this is a specialized network rather than a general-purpose one, we can reduce the topology to a two-hop network and compute the total capacity based on the inflow and outflow from each IPN node, which acts as the minimum cut or bottleneck.

The inflow for an IPN node *x*, representing the supply of data from other nodes, is defined as

$$inflow_x = \sum_{o}^{O_{Mars}} flow_{o,x}, \quad (2)$$

where $O_{Mars}$ is the set of all Mars science orbiter satellites that generate data.

<figure><img src="image_4.png" /><figcaption>Fig. 3: Adjacency matrix of contact edge weights.</figcaption></figure>

Outflow then represents the demand for the data node *x* currently has, and is defined by

$$outflow_x = \sum_{x'}^{X_{Earth}} flow_{x,x'}, \quad (3)$$

where $X_{Earth}$ is the set of all relay satellites orbiting Earth that are the destination nodes for all data generated on Mars.

The flow capacity of the network can be thought of as the balance between these two equations and is fully defined as

$$C = \sum_{x}^{X_{Mars}} \min(inflow_x, outflow_x), \quad (4)$$

where the set of relay satellites orbiting Mars are $X_{Mars}$. The objective function of LLS is to maximize the total satisfiable demand across the entire contact plan for all Mars relay nodes.

Figure 3 shows the adjacency matrix representation of the average edge weights across all *K* states. The white cells indicate no contact opportunities between the transmitting and receiving nodes. Blue and purple cells are edges that exist but never result in an increase in network capacity. These are due to intra-constellation ISLs or non-backhaul interplanetary links, with Earth as the source and Mars as the destination. The green and yellow cells are edges that contribute to an increase in network capacity and are isolated to contact opportunities between i) the Mars orbiters to the Mars relays; and ii) the Mars relays to the Earth relays. The graph shows that our model produces the expected behavior: Mars orbiter nodes create network capacity when transmitting data to Mars relay nodes, and the Mars relay satellites also create network capacity when transmitting data to the Earth relays. This verifies that our capacity model correctly models the backhaul network capacity by giving the highest weights to the edges, which will, in practice, result in the highest overall increase in capacity.

### III. EVALUATION

The lasers have a bit rate of 267 megabits per second as tested in the NASA Deep Space Optical Communication

<footnote n="1">¹https://github.com/jason-gerard/laser-link-scheduler</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 4: Performance comparison of algorithms across different sized networks over 24 hours.</figcaption></figure>

<table><caption>TABLE I: Constellations and shells.</caption><thead><tr><th>Name</th><th>Shell</th><th>Constellation type</th><th>Altitude</th></tr></thead><tbody><tr><td>Earth relay</td><td>-</td><td>Walker delta</td><td>500 km</td></tr><tr><td>Mars relay</td><td>Outer</td><td>Walker star</td><td>1,500 km</td></tr><tr><td>Mars science</td><td>Inner</td><td>Walker delta</td><td>500 km</td></tr></tbody></table>

<table><caption>TABLE II: Evaluation scenarios in Walker notation.</caption><thead><tr><th>Scenario</th><th>Earth relay</th><th>Mars relay</th><th>Mars science</th></tr></thead><tbody><tr><td>X-Small</td><td>45°: 1/1/1</td><td>90°: 1/1/1</td><td>45°: 4/2/1</td></tr><tr><td>Small</td><td>45°: 2/1/1</td><td>90°: 4/2/1</td><td>45°: 16/4/1</td></tr><tr><td>Medium</td><td>45°: 4/2/1</td><td>90°: 6/3/1</td><td>45°: 64/8/1</td></tr><tr><td>Large</td><td>45°: 4/2/1</td><td>90°: 8/4/1</td><td>45°: 128/16/1</td></tr><tr><td>X-Large</td><td>45°: 8/4/1</td><td>90°: 16/4/1</td><td>45°: 192/16/1</td></tr></tbody></table>

(DSOC) Mars to Earth experiment, with the assumption that there is no time penalty for contact switching [16]. LLS is compared against the Fair Contact Plan (FCP) algorithm, the alternating scheduler (naive) algorithm, and the random scheduler algorithm, observed over 24 hours. FCP maximizes fairness in a given constellation, but this solution does not translate to an interplanetary backhaul communication system since the network is not homogeneous. The naive approach alternates each round between randomly scheduling intra-constellation transmissions and inter-constellation transmissions. The random scheduler gets a baseline schedule without any knowledge of the topology. Table II contains the five evaluation scenarios with different sizes of networks, all using the same constellation structures defined in Tab. I. Each cell in the table is defined in Walker notation, indicating inclination, total number of satellites, number of planes, and plane spacing. The contact plans are generated using the IPN-V tool, the open-source repository is publicly available 2 [18].

Figure 4a shows the scheduled network capacity, C, defined by Eq. 4, which measures the amount of data that can be backhauled to Earth within the scheduled contact plan L. Scheduled capacity is the primary metric of concern when evaluating networks used for backhaul. LLS achieves 1.5-2x the capacity of FCP while providing a fully synchronized contact plan enabling ATP. Even for small networks, where the bottleneck contains 2-6 nodes, resulting in a limited number of decision points, LLS makes accurate choices about the optimal selection of edges in each state. The X-Large scenario

contains over 200 nodes and has a contact plan input file of 11.2 megabytes; LLS executes in a little under 30 minutes on consumer-grade hardware due to the dynamic programming-based capacity model implementation, which allows it to be practically deployed in an MOC and on user devices for scenario planning. Interestingly, as the network size increases, LLS has even higher capacity than the other solutions, showing that LLS can scale to large networks in terms of both scheduled contact plan quality, measured by backhaul network capacity, and real-world hardware performance.

Figure 4b depicts the wasted buffer capacity, B, which is the amount of data transmitted to the Mars relays from the Mars orbiters but was not scheduled to be transmitted to an Earth relay, defined as

$$B = \sum_{x}^{X_{Mars}} \max(inflow_{x} - outflow_{x}, 0). \quad (5)$$

Wasted buffer capacity gives insight into where the bottleneck or minimum cut is in the network. If B is high, then the Mars orbiter to Mars relay links will be over-allocated proportionally to the interplanetary links. This is the case for FCP and Alternating, which see a significant increase proportional to the size of the scenario. This indicates that they are allocating more contact time to intra-constellation links as the scenarios grow. LLS, however, has a near-zero and constant wasted buffer for all scenarios, indicating that it avoids the overallocation of Mars relay buffers and allows them to clear before reallocating. LLS leverages the maximum flow model to avoid a significant bottleneck at the interplanetary link segment of the network. A low wasted buffer indicates high energy efficiency due to reduced wasted transmissions.

Scheduled delay, shown in Fig. 4c, measures the average wait time between contact opportunities from a Mars orbiter to a Mars relay satellite, and is defined as

$$D = \left( \sum_{o}^{O_{Mars}} \frac{\sum_{x}^{X_{Mars}} DCT_{o,x}}{q_{o}} \right) \cdot \frac{1}{O_{Mars}}, \quad (6)$$

where $q_o$ is the number of discrete contacts in L for the o-th node. LLS and FCP show similar results, with delays of less than 60 minutes. This is because D measures intra-constellation delay, and both algorithms account for the DCT of each intra-constellation edge. In the case of LLS, DCT

<footnote>2https://gitlab.inria.fr/jfraire/ipn-v</footnote>
will act as a tie-breaker between edges that produce the same increase in network capacity. If an edge is not selected, its weight increases, making it more likely to be selected. This actively encourages the selection of edges with a higher overall delay, reducing the standard deviation between individual node delays. Interestingly, the delay is correlated with the maximum buffer size required and the average buffer fullness that the node can expect while in operation. Even if the data is very delay-tolerant, keeping the buffer below a certain threshold will still be needed due to mission SWaP requirements.

JFI measures the fairness between the Mars orbiters concerning the amount of data transmitted to the Mars relay satellites, defined as

$$JFI = \frac{(\sum_{o}^{O_{Mars}} \sum_{x}^{X_{Mars}} flow_{o,x})^2}{O_{Mars} \cdot \sum_{o}^{O_{Mars}} (\sum_{x}^{X_{Mars}} flow_{o,x})^2} \quad (7)$$

Fairness is maximized if all Mars orbiters are scheduled to transmit the same amount of data to the Mars relay satellites. Similar to delay, the fairness of LLS and FCP are matched at around 0.97 due to both incorporating DCT into their weight computations. This acts as a proxy metric for delay and fairness since the lasers' data rates are all equal. This will not be the case in future experiments with heterogeneity in the lasers. The bit rate term in Eq. 1 will act as a vector of linear weights on the DCT. However, the average scheduled delay will remain the same since the bit rate is not included in the metric's computation.

## IV. CONCLUSION

Deep-space communication networks have limited capacity, necessitating scheduled laser transmissions. Currently, operators at MOCs manually schedule transmission opportunities, a process that is inefficient and prone to error for small networks and infeasible for large networks. This limits the parallelization of space exploration missions. Although autonomous contact scheduling offers a potential solution, it risks overlooking optimal network routes. In this paper, we designed a scheduling algorithm, Laser Link Scheduler (LLS), which uses a maximum-flow network capacity model to prioritize the directional flow of data toward Earth. This maximizes the interplanetary backhaul network capacity while minimizing wasted buffer capacity. This method achieves 1.5-2x the network capacity of other autonomous scheduled algorithms while maintaining high fairness and delays of 60 minutes. Future work will explore the potential of multi-laser communication and applying LLS to highly distributed topologies representing deep space missions executed in parallel.

## ACKNOWLEDGMENT

The authors would like to acknowledge the support from Concordia's PhD Fellowship Award, NSERC Canada Discovery Grant RGPIN-2024-05730, ANID Basal Project AFB240002, and the French National Research Agency (ANR) under project ANR-22-CE25-0014-01. They would also like to thank Dr. Rachel Dudukovich, Dr. Daniel Raible,

Dr. Bob Short, and the rest of the HDTN team at the NASA Glenn Research Center for their comments and input on this work.

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