Admission control is important in achieving QoS guarantees in multi-hop wireless networks. An efficient admission control algorithm requires an accurate estimation of the end-to-end delay distribution of the network. In this paper, we propose a method to estimate the end-to-end delay distribution under the general traffic arrival process and Nakagami-m channel model. We firstly propose a novel two-dimensional Markov Chain to model the node behaviors in a multi-hop multi-rate IEEE 802.11 network that is subject to interference and error prone channel. By combining the basic Probability theory and Network Calculus, we analyze the delay a packet experiences at each hop along a path. The per-hop delay result is used to develop the distribution of the end-to-end delay of a randomly chosen path. We then develop an admission control scheme for the traffic with stochastic QoS guarantees. Finally, through simulation results, we verify the accuracy of our analytical model and the effectiveness of the proposed algorithm.

Accurate channel state information (CSI) is essential to increasing throughput in multi-input, multi-output (MIMO) systems with digital beamforming. CSI can be acquired by channel estimation and reported via feedback mechanisms. While the training and feedback overhead are typically proportional to the number of antennas, uplink measurements can be utilized to predict downlink CSI assuming perfect channel reciprocity in a time division duplex (TDD) mode. However, many works make the assumption that channels are perfectly reciprocal, which is often not the case in practice due to poor channel estimation and physical channel asymmetry. In this work, we investigate the key challenges in channel feedback performance, including TX-RX imbalance, channel coherence, and interference for mobile systems. We evaluate IEEE 802.11ac-based implicit and explicit feedback schemes with both emulated and in-field MIMO channels, particularly in regards to drone-based transmissions. Our analysis with channel emulation shows that implicit feedback is susceptible to channel reciprocity errors, while explicit feedback is more sensitive to Doppler effects. We propose a hybrid feedback mechanism that increases the throughput by 32% over conventional feedback methods. We additionally evaluate the impact of frequency offset asymmetry on the performance of distributed mobile systems. Our in-field experiments demonstrate that explicit feedback can provide better throughput improvement than implicit feedback in highly-mobile air-to-ground channels. Since our study spans many critical frequency bands, these results serve as a fundamental step towards understanding the impacts of asymmetric factors of channel reciprocity for drone-based beamforming systems.