Saturation Throughput and Delay Performance Evaluation of the IEEE 802.11g/n for a Wireless Lossy Channel

Non-ideal channel conditions degrade the performance of wireless networks due to the occurrence of frame errors. Most of the well-known works compute the saturation throughput and packet delay for the IEEE 802.11 Distributed Coordination Function (DCF) protocol with the assumption that transmission is carried out via an ideal channel (i.e., no channel bit errors or hidden stations), and/or the errors exist only in data packets. Besides, there are no considerations for transmission errors in the control frames (i.e., Request to Send (RTS), Clear to Send (CTS), and Acknowledgement (ACK)). Considering the transmission errors in the control frames adds complexity to the existing analysis for the wireless networks. In this paper, an analytical model to evaluate the Medium Access Control (MAC) layer saturation throughput and packet delay of the IEEE 802.11g and IEEE 802.11n protocols in the presence of both collisions and transmission errors in a non-ideal wireless channel is provided. The derived analytical expressions reveal that the saturation throughput and packet delay are affected by the network size (n), packet size, minimum backoff window size (Wmin), maximum backoff stage (m), and bit error rate (BER). These results are important for protocol optimization and network planning in wireless networks.

THROUGHPUT AND BOTTLENECK ANALYSIS OF TANDEM QUEUES WITH NESTED SESSIONS

Various types of systems across a broad range of disciplines contain tandem queues with nested sessions. Strong dependence between the servers has proved to make such networks complicated and difficult to study. Exact analysis is in most of the cases intractable. Moreover, even when performance metrics such as the saturation throughput and the utilization rates of the servers are known, determining the limiting factor of such a network can be far from trivial. In our work, we present a simple, tractable and nevertheless relatively accurate method for approximating the above mentioned performance measurements for any server in a given network. In addition, we propose an extension to the intuitive “slowest server rule” for identification of the bottleneck, and show through extensive numerical experiments that this method works very well.