Throughput Optimization of Heterogeneous IEEE 802.11 DCF Networks

Abstract

This paper presents the throughput analysis of an M -group heterogeneous IEEE 802.11 DCF network where nodes in different groups have distinct input rates and initial backoff window sizes. An explicit expression of the network steady-state operating point is obtained based on the fixed-point equation of the limiting probability of successful transmission of Headof-Line (HOL) packets given that the channel is idle, which is shown to be closely dependent on the backoff parameters of saturated groups and the input rates of unsaturated groups. Both the network throughput and the group throughput performance are further characterized, and the maximum network throughput is derived as an explicit function of the holding times of HOL packets in successful transmission and collision states. The analysis reveals that to achieve the maximum network throughput, the optimal set of input rates of unsaturated groups and initial backoff window sizes of saturated groups should satisfy a constraint that is determined by the group sizes of saturated groups. Given the input rates of unsaturated groups, for instance, the initial backoff window sizes of saturated groups should linearly increase with their group sizes, and those with higher increasing rates achieve lower group throughput.

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

“…According to work in [11,27,28], the contention of accessing the channel can be modeled as a discrete time stochastic process that includes three states: idle slot state, successful packet transmission state including RTS/CTS/DATA/ACK and failed transmission state indicated by a failed RTS. The probabilities of the above states are three basic parameters in CSMA/CA based channel access schemes.…”

Maximizing the Stable Throughput of Heterogeneous Nodes Under Airtime Fairness in a Csma Environment

“…It stems also from device evolution, i.e., multi-sensor platforms in wireless sensor networks produce a variety of packet sizes originating from a same node [17]. Heterogeneous CSMA performance evaluation and optimization has been considered extensively, since the key contribution [18], that introduced a capacity-achieving ''adaptive CSMA'' algorithm, e.g., see [15,[19][20][21][22][23][24][25][26][27]. Some of those works consider fixed transmission times [20,21] or assume zero sensing time [15,18,22,24,27] and hence disregard collision events (idealized CSMA, where it is also usually assumed that packet transmission times are exponentially distributed).…”

“…Heterogeneous CSMA performance evaluation and optimization has been considered extensively, since the key contribution [18], that introduced a capacity-achieving ''adaptive CSMA'' algorithm, e.g., see [15,[19][20][21][22][23][24][25][26][27]. Some of those works consider fixed transmission times [20,21] or assume zero sensing time [15,18,22,24,27] and hence disregard collision events (idealized CSMA, where it is also usually assumed that packet transmission times are exponentially distributed). All of those works, even in case they account for collision events, assume a fixed collision time, irrespective of which stations are involved, i.e., they assume ''probe'' packets are used similarly to RTS/CTS mode in IEEE 802.11 MAC protocol.…”

AID‐based backoff for throughput enhancement in 802.11ah networks

“…For instance, in Yang et al, STAs are grouped based on their data rate with each group having different initial backoff window, frame size, and maximum backoff stage in order to improve throughput by fairly allocating the channel occupancy time . In Gao et al, STAs are grouped according to their saturation status, where it has been shown that in order to maximise throughput, the initial backoff window should be increased for saturated groups. This is in agreement with Bianchi's finding that the optimal window size is proportional to the number of contending STAs .…”