Lukman A. Olawoyin

Standard Propagation Model Tuning for Path Loss Predictions in Built-Up Environments

This paper provides a simple optimization procedure using ATOLL planning tool for Standard Propagation Model (SPM). Measurement campaigns were conducted to collect Received Signal Strength (RSS) data over commercial base stations operating at 1800 MHz. The prediction accuracy of widely used models were assessed. The models provided high prediction errors. The optimization procedure involves the use of Digital Terrain Model (DTM), clutter classes, clutter heights, vector maps, scanned images, and Web Map Service (WMS). A Logarithmic weighting function was used to calculate the weight of the clutter loss on each pixel from the pixel with the receiver in the direction of the transmitter, up to the defined maximum distance. The approach has proven promising by achieving high accuracy and minimizing the prediction errors by 47.4%.

Adaptive Neuro-Fuzzy model for path loss prediction in the VHF band

Path loss prediction models are essential in the planning of wireless systems, particularly in build-up environments. However, the efficacies of the models depend on the local ambient characteristics of the environments. This paper proposed the Neuro-Fuzzy (NF) model for path loss prediction for Ilorin in the VHF band. Received signal strengths along four different routes were measured using NTA Ilorin transmitter which operates at a frequency of 203.25 MHz as a reference. The predictions of the proposed model was compared to Hata, COST 231, Egli and ECC-33 models which are considered standard and widely used empirical path loss models. The Root Mean Square Error (RMSE) was used as a measure of merit for their performances. Across all the routes visited, an average RMSE of 5.253 dB, 9.487 dB, 14.264 dB, 18.696 dB, and 27.890 dB were obtained respectively for the NF, ECC-33, Hata, COST 231 and Egli models. The NF model result is shown to improve the predictions over the estimates obtained when compared with the other models.

Energy Efficient Dynamic Bid Learning Model for Future Wireless Network

In this paper, an energy efficient learning model for spectrum auction based on dynamic spectrum auction process is proposed. The proposed learning model is based on artificial intelligence. This paper examines and establishes the need for the users to learn their bid price based on information about the previous bids of the other users in the system. The paper shows that using Q reinforcement learning to learn about the bids of the users during the auction process helps to reduce the amount of energy consumed per file sent for the learning users. The paper went further to modify the traditional Q reinforcement learning process and combined it with Bayesian learning because of the deficiencies associated with Q reinforcement learning. This helps the exploration process to converge faster thereby, further reducing the energy consumption by the system.

A random linear code based secure transmission scheme for wireless fading channels

摘要创新点针对无线衰落信道中物理层传输的安全问题, 提出了一种基于随机线性码的解决方案。 所提方案中, 发送方 (A) 使用的编码生成多项式由接收方 (B) 产生并发送给A。 生成多项式的产生满足一定的规则, 使得窃听方 (E) 如果漏掉A、 B双方数据包中的任何一个, 则无法解出任意一个信源数据包, 从而可以显著降低窃听方截获信息的概率。