Sub-Messages Scheduling in GNSS Packet Broadcasting By Message Splitting

Ardizzon, Francesco; Laurenti, Nicola; Tomasin, Stefano

doi:10.1109/icl-gnss49876.2020.9115495

Cited by 2 publications

(7 citation statements)

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“…We also consider the problem of maximizing the average received packets per round, for which we first quantize the coverage area and then obtain a BILP problem. Numerical results show the validity of our solution and the improvement with respect to the solution in [16], obtaining reduced average and maximum latency.…”

Section: Introductionmentioning

confidence: 63%

“…Let K n be the number of different packets transmitted at round n and not transmitted in previous rounds, so that n K n = K. Then, K n is chosen as the maximum number of packets that can be transmitted in a single round with full coverage. To this end, we iteratively increase K n starting from K = 1 and resort to the BILP algorithm of [16] to compute the corresponding coverage γ, until we reach full coverage. The resulting solution is reported in Algorithm 1, where K ′ = K− n K n represents the number of packets left to be transmitted.…”

Section: A Minimization Of the Maximum Latency (Min-max)mentioning

confidence: 99%

“…In the algorithm we exploit the following functions: comp_visibility_maps : computes the satellite positions at round n and outputs the visibility maps, v s,n (x); solve_max_cov : implements the (single round) coverage optimization [16], which we have shown to be effective for small K values.…”

Section: A Minimization Of the Maximum Latency (Min-max)mentioning

confidence: 99%

“…Algorithm 1 follows a divide-and-conquer approach. If the algorithm is iterated for n rounds, the overall computational cost is n times the cost of the single round scheduling solution, which from [16] is exponential in the size |A| and the number of packets to be transmitted, K n ≤ C min,n . However, since C min,n is typically small (C min,n ≈ 4), in practice we expect the algorithm to converge quickly.…”

Section: A Minimization Of the Maximum Latency (Min-max)mentioning

confidence: 99%

“…Defined coverage as the fraction of area from which they receive the whole message, in [16] the authors proposed a solution to maximize the coverage for fast message transmission, i.e., the fraction of receivers that can obtain all the packets and reconstruct the message at once. However, it was shown that no scheduling strategy could achieve full coverage and reliable reception for 4 packets at the same time with the Galileo system, therefore this solution is only effective for short messages.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Round Message Scheduling for Fast GNSS Packet Broadcasting

Ardizzon

Laurenti

Tomasin

2023

IEEE Trans. Aerosp. Electron. Syst.

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Global navigation satellite systems (GNSSs) may transmit data to provide additional services. Since the resources reserved for these data are typically fixed and the message rate is low, we propose to split long messages into shorter packets and properly schedule their transmission from the entire constellation of satellites. Considering an efficient data transmission (useful for example for search and rescue messages), we aim at the scheduling of packets on the satellites on multiple rounds with two objectives: either a) the minimization of the maximum latency among all receivers, or b) the maximization of the average received packets per round. We first derive bounds on the performance of any GNSS single or multi-round scheduling solution, on which the proposed scheduling solutions are based. Then we introduce the scheduling problems that turn out to be integer linear programming (ILP) problems. Lastly, we assess their performance, showing that our solution minimizes the maximum latency, while the scheduling targeting the average latency outperforms existing literature solutions.

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Section: Introductionmentioning

confidence: 63%

Section: A Minimization Of the Maximum Latency (Min-max)mentioning

confidence: 99%

Section: A Minimization Of the Maximum Latency (Min-max)mentioning

confidence: 99%

Section: A Minimization Of the Maximum Latency (Min-max)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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