Joint Modeling of Throughput, Service Time, and Queue Length in IEEE 802.11 WLANs with Frame Aggregation and Unsaturated Traffic Load

Sensitivity-driven Adaptive Contention Window Optimization for IEEE 802.11 based V2I Networks

In vehicle-to-infrastructure (V2I) communication the setting of IEEE 802.11 Distributed Coordination Function (DCF) parameters has a decisive bearing on performance, yet the literature seldom pins down how much each parameter actually matters once traffic, MAC and queueing are modelled together. Treating a previously validated analytical framework as a fixed deterministic input-output map, we rank the DCF and traffic parameters that shape throughput, collision probability, delay, packet delivery ratio and Age of Information in a single-AP V2I network. A local one-factor-at-a-time analysis, cast in dimensionless elasticities so that parameters of different units become comparable, is paired with a variance-based global analysis built on first-order and total-effect Sobol indices. Two clean groups emerge: collision probability is set by the contending-vehicle population -- itself governed by vehicle velocity and density -- together with the minimum contention window, whereas delay is driven by the channel rate, the offered load and the packet size, and carries strong interaction effects that no local reading can expose. We then derive the closed-form structure of these sensitivities from the model relations, which explains the rankings, forces certain parameters into equal-magnitude elasticities, and locates where the local ranking reverses. Finally the collision-sensitivity structure is turned into a design output rather than a ranking: a closed-form contention-window control law, linear in the contending population and closed with a Greenshields density model, that a roadside access point can evaluate online from measured density or velocity. The fixed IEEE 802.11 default is recovered as the single population at which this law is optimal; away from it the throughput gain grows with density and is largest in the dense, safety-critical regime.

Delay and Throughput Analysis of Prioritized EDCA in Latency-Sensitive Wi-Fi 8 Networks

Wi-Fi 8 introduces Prioritized EDCA (P-EDCA) to support latency-critical traffic within which DS-RTS/CTS operates as a two-phase channel access procedure. In this mechanism, stations first compete in a Defer Signal (DS) contention, which determines the number of stations entering RTS/CTS contention, creating a stochastic coupling not captured by existing IEEE 802.11 models. We develop an analytical framework for the performance analysis of DS-RTS/CTS using a 2-D Markov chain to model the RTS backoff process of a tagged station under a variable number of contenders. Using this model, we obtain expressions for head-of-line delay and normalized throughput. We further formulate an optimization problem for adaptive selection of the DS contention window size. Results show that a moderately sized DS contention window achieves a favorable throughput-delay trade-off across network densities.

Traffic-Aware Distributed Resource Unit Selection for Uplink OFDMA Random Access in IEEE 802.11ax WLANs

IEEE 802.11ax adopts Orthogonal Frequency Division Multiple Access (OFDMA), which divides a wireless channel into multiple Resource Units (RUs) with different sizes and transmission rates. Most existing studies on uplink random access assume that all stations contend for RUs of the same size and mainly improve channel access by adjusting contention parameters, such as the OFDMA backoff mechanism. As a result, the transmission requirements of stations are not sufficiently considered, leading to inefficient RU utilization. Stations with heavy traffic may not obtain adequate transmission resources, whereas stations with light traffic may occupy unnecessarily large RUs. To address this limitation, this paper proposes a Traffic-Aware Distributed Selection (TADS) scheme for IEEE 802.11ax uplink random access with different RU sizes. Each station estimates its traffic demand based on the mean data rate, delay bound, and buffer status. It also estimates the expected service capacity of each RU size using the transmission capacity and collision probability. The station selects the smallest RU size whose expected service capacity satisfies its traffic demand. If no RU size can satisfy the demand, the RU size with the highest ratio of expected service capacity to traffic demand is selected. When multiple RUs of the selected size are available, the station randomly selects one of them. In TADS, each station determines its RU independently without additional scheduling by the access point. Simulation results show that TADS achieves higher network throughput than conventional random RU selection under data, real-time, and mixed traffic scenarios. The results also show that the performance gain depends on the available RU configuration, with greater benefits when different RU sizes are available.

Adaptive Scheduling for Low-Latency Coordination in Wi-Fi 8 Multi-AP Networks

Reliable low-latency communication is a critical requirement in enterprise wireless networks such as hospitals, offices, and campuses. This paper proposes an earliest deadline first (EDF)-Lyapunov-Robbins-Monro (ELR), a stochastic scheduling algorithm for IEEE 802.11bn (Wi-Fi 8) Multi-Access Point Coordination Coordinated-Spatial Reuse (MAPC C-SR) networks that jointly accounts for queue stability and deadline-aware latency regulation under bursty traffic. A Lyapunov drift-based criterion for a group is adopted to ensure queues remain stable under varying traffic loads. Since the optimal balance between queue backlog and deadline urgency cannot be determined a priori under bursty traffic, EDF term is incorporated into the selection metric with a tunable balance parameter $\alpha$, governed by Robbins-Monro stochastic approximation scheme. The proposed algorithm addresses the inability of existing schedulers to track sudden congestion under bursty traffic, by dynamically adjusting $\alpha$ to suppress sharp delay spikes. Simulations over a four-access point (AP) enterprise deployment under bursty Markov-Modulated Poisson Process (MMPP) traffic demonstrate that ELR achieves 14.23%, 13.26%, and 7.97% reduction in 99th percentile delay over maximum number of packets (MNP), oldest packet (OP), and traffic alignment tracker (TAT) respectively under high load with 16 stations (STAs).

New estimation for the tra ffi c intensity parameter of a single-server queueing system with finite capacity

Load Balancing with Individually Heterogeneous Servers

In this paper, we analyze the diffusion limit of Join-the-Shortest-Queue (JSQ) load balancing policies for a system with many parallel queues where each queue is being served by a server with a potentially different service rate. Prior asymptotic analyses of JSQ policies assumed servers to have service rates from a finite set which does not capture scenarios in modern data centers where service rates can vary at the level of individual servers. For systems with individually heterogeneous servers, tracking the empirical queue length distribution for each possible service rate becomes infeasible. To overcome this difficulty, we develop a framework based on measure-valued processes and provide a unified analysis of all JSQ-based load balancing policies under general tie-breaking rules. Our analysis identifies two key objects that distinguish the performance of different tie-breaking rules in the Halfin--Whitt regime, namely, the limiting fairness process which describes how idle servers are distributed across different service rates and the limiting routing measure which describes how arriving jobs are assigned across different service rates. In addition to characterizing the diffusion limits for different tie-breaking rules, we identify the tie-breaking rule which asymptotically minimizes the steady-state distributions of the diffusion-scaled total number of jobs and the diffusion-scaled number of waiting jobs. In proving these results, we develop crucial coupling-based sample-path comparisons which provide both policy-independent steady-state bounds and lower bounds to prove asymptotic optimality.