This paper presents a CAC algorithm based on a novel estimation method, called Quasi-Linear Dual-class Correlation (QLDC). All traffic calls are categorized into classes. With the number of calls in each class, QLDC conservatively and precisely estimates the cell delay and cell loss ratio of each class via simple vector multiplication. These vectors are derived in advance from the results of three dual-arrival queueing models, namely M[N(1)] + I[N(2)]/D/1/K, M1[N(1)] + M2[N(2)]/D/1/K, and I1[N(1)] + I2[N(2)]/D/1/K, where M and I represent Bernoulli and Interrupted Bernoulli processes, respectively. Consequently, our QLDC-based CAC yields low time complexity O(C) (in vector multiplications) and space complexity O(WC2) (in bytes), where C is the total number of traffic classes and W is the total number of aggregate-load levels. We also use numerical examples to justify that QLDC-based estimated results profoundly agree with simulation results in both the single-node and end-to-end cases.
|Number of pages||5|
|State||Published - 1 Jan 1996|
|Event||Proceedings of the 1996 IEEE International Conference on Communications, ICC'96. Part 1 (of 3) - Dallas, TX, USA|
Duration: 23 Jun 1996 → 27 Jun 1996
|Conference||Proceedings of the 1996 IEEE International Conference on Communications, ICC'96. Part 1 (of 3)|
|City||Dallas, TX, USA|
|Period||23/06/96 → 27/06/96|