Abstract
Frame aggregation is central to modern IEEE 802.11 networks, yet the existing performance models fail to capture how it behaves under usual unsaturated traffic. Some rely on a predefined service-time distribution; others cover only narrow unsaturated cases, such as stations withholding transmission until K packets accumulate or stations being modeled as if they always have a packet queued. This paper develops a performance model for 802.11 networks with frame aggregation under unsaturated traffic in which the aggregation size and service time emerge dynamically from the offered traffic load, the random backoff process, and the number of stations rather than from any of these simplifying assumptions. Beyond throughput, the model derives closed-form estimates of the average aggregation size, service time, and per-station queue length directly from the steady-state distribution of a three-dimensional Markov chain. Performance evaluations across two physical-layer rates (867 and 150 Mbps), two queue capacities, and different numbers of stations show that the proposed model produces throughput and aggregation-size estimates that closely match an event-driven simulator, while the service time and queue-length estimates reflect the model’s own assumption.