Access Point Deployment Optimization in CBTC Data Communication System

Abstract

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A Practical Access Point Deployment Optimization Strategy in Communication-Based Train Control Systems

“…In our prior work [20] a method to assess the feasibility and the overall performance of AP deployments has been established and successfully validated. In this method, we avoid a priori assumption on what constitutes a "nonsensical" deployment, all the AP deployment combinations are considered, the outage probability is utilized as the criterion to select the best deployments, the outage probability at each sampling point need to be assured as lower than the prespecified threshold,…”

“…All the feasible AP deployments form S. Additionally, we assume that the safety-critical operation is also achieved through hardware redundancy at each AP node. In this paper, instead of using experience-based system optimisation factor to add preference on objectives in [20], the AP deployment optimization problem is formulated as:…”

“…By consolidating and extending the existing radio propagation models [15], [16] and [17], and considering the experiments conducted under a railway-specific environment [18], [3] and [19], an AP deployment optimization method for CBTC systems was proposed in our prior work [20]. However, only brute-force exhaustive search was implemented in [20], which is computationally infeasible in planning largescale DCS systems. Therefore, we have a strong motivation to further extend our prior work.…”

State Estimation for Communication-Based Train Control Systems With CSMA Protocol

“…It is worth mentioning that a guaranteed ultimate bound of the estimation error dynamics can be associated with a kind of estimation performance about the "attenuation" against the effect of perturbations. On the other hand, it can be seen from (14) that the decay rate of the estimation error (i.e. 1−γ ) should be large enough to guarantee the ultimate boundedness of e(k).…”

“…Based on the Schur Complement Lemma (Lemma 1) and the desired state estimator gain in (27), it can be concluded that the inequality (14) holds with the condition γ = 1 − θ if and only if the inequality (23) is satisfied. Hence, it follows from Theorem 1 that the dynamics of the estimation error e(k) is ultimately bounded in mean square.…”