The paper proposes and analyzes an adaptive p-persistent-based (APP) medium access control (MAC) scheme for IEEE 802.11 WLAN. The APP MAC scheme intends to support delay fairness for every station in each access, denoting small delay variance. It differentiates permission probabilities of transmission for stations which are incurred with various packet delays. This permission probability is designed as a function of the numbers of retransmissions and re-backoffs so that stations with larger packet delay are endowed with higher permission probability. Also, the scheme is analyzed by a Markov-chain analysis, where the collision probability, the system throughput, and the average delay are successfully obtained. Numerical results show that the proposed APP MAC scheme can attain lower mean delay and higher mean throughput. In the mean time, simulation results are given to justify the validity of the analysis, and also show that the APP MAC scheme can achieve more delay fairness than conventional algorithms.

In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems. The proposed model integrates the feature learning block, recurrent neural network (RNN), and connectionist temporal classification loss to build an end-to-end ASR model. Facing the complex environments of ATC speech, instead of the handcrafted features, a learning block is designed to extract informative features from raw waveforms for acoustic modeling. Both the SincNet and 1D convolution blocks are applied to process the raw waveforms, whose outputs are concatenated to the RNN layers for the temporal modeling. Thanks to the ability to learn representations from raw waveforms, the proposed model can be optimized in a complete end-to-end manner, i.e., from waveform to text. Finally, the multilingual issue in the ATC domain is also considered to achieve the ASR task by constructing a combined vocabulary of Chinese characters and English letters. The proposed approach is validated on a multilingual real-world corpus (ATCSpeech), and the experimental results demonstrate that the proposed approach outperforms other baselines, achieving a 6.9% character error rate.

The existing carrier sensing multiple access (CSMA) based wireless networks cannot realize the capture effect functionality. Consequently, transmitters within the physical carrier sensing (PCS) range of a receiver cause interference to its reception, which is referred to as the pseudo capture effect. Such interference severely degrades the system performance because the default PCS range is usually quite large. Therefore the PCS range should be adjusted to reduce the packet loss caused by pseudo capture effect. In order to guide the optimal PCS range setting, a modified p-persistent model is proposed in this paper to investigate the throughput of CSMA-based networks considering pseudo capture effect. Simulation results show that the proposed model accurately evaluates the influence of pseudo capture effect. By utilizing the model, we observe that the optimal PCS range considering pseudo capture effect is smaller than the case without considering its impact.