Multi-Satellite NOMA-Irregular Repetition Slotted ALOHA for IoT Networks

As the transition from 5G to 6G unfolds, a substantial increase in Internet of Things (IoT) devices is expected, enabling seamless and pervasive connectivity across various applications. Accommodating this surge and meeting the high capacity demands will necessitate the integration of NonTerrestrial Networks (NTNs). However, the extensive coverage area of satellites, relative to terrestrial receivers, will lead to a high density of users attempting to access the channel at the same time, increasing the collision probability. In turn, the deployment of mega constellations make it possible for ground users to be in visibility of more than one satellite at the same time, enabling receiver diversity. Therefore, in this paper, we evaluate the impact of multi-receivers in scenarios where IoT nodes share the channel following a non-orthogonal multiple access (NOMA)irregular repetition slotted ALOHA (IRSA) protocol. Considering the impairments of satellite channels, we derive a lower bound of system performance, serving as a fast tool for initial evaluation of network behavior. Additionally, we identify the trade-offs inherent to the network design parameters, with a focus on packet loss rate and energy efficiency. Notably, in the visibility of only one extra satellite as receiver yields significant gains in overall system performance.

💡 Research Summary

The paper investigates the performance of a satellite‑based Internet‑of‑Things (IoT) uplink that combines two emerging concepts: non‑orthogonal multiple access (NOMA) power‑domain diversification and the irregular repetition slotted ALOHA (IRSA) random‑access protocol, while also exploiting the spatial diversity offered by multiple low‑Earth‑orbit (LEO) satellites acting as receivers. The authors model the system under realistic satellite channel impairments using an on‑off fading (OOF) model, where each user‑satellite link experiences an independent erasure probability ε (e.g., due to rain fade or blockage).

System model – A population of m uncoordinated IoT devices each transmits ℓ̅ replicas of a short packet within a MAC frame of n slots. The number of replicas ℓ is drawn from a predefined probability mass function Λ(x)=∑Λℓxℓ (e.g., Λ(x)=0.25x²+0.75x³). Each replica is transmitted at either a “strong” or a “weak” power level. The strong level p₁=αP and the weak level p₂=(1‑α)P are fractions of a peak power constraint P, with α derived from the target SINR γ so that the strong signal can be decoded even when a weak signal collides in the same slot. The choice of power level is equiprobable for each replica.

Receiver operation – Each satellite independently runs a successive interference cancellation (SIC) algorithm over the whole frame. A replica is decoded if its instantaneous SINR exceeds γ; this occurs when the replica is alone in a slot or when it is the stronger of two colliding replicas (capture effect). Once decoded, the receiver knows the positions of all other replicas of the same user (the packet header contains pointers) and removes their contribution from the corresponding slots, potentially turning previously collided slots into singleton slots for subsequent iterations. The SIC process repeats until no further decodable replicas remain.

Multi‑receiver model – With k ≥ 1 satellites, each observes a different erasure pattern because the OOF channel realizations are independent across satellites. However, replicas belonging to the same user share the same erasure outcome on a given satellite (i.e., either all are erased or all survive). After each satellite finishes its local SIC, the sets of successfully decoded users Dj are aggregated; the total number of decoded users is d=|⋃j Dj|.

Performance metrics – Two key metrics are studied:

1. Packet loss rate (PLR) – Defined as the probability that a randomly chosen user is not decoded by any satellite. By assuming independence among satellites (which yields a lower bound), the authors obtain
   PLR_B(G) =