Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

Preliminary Results of Solar Flare Induced D-Region Perturbations over UKM Using Stanford AWESOME Receiver

We present the preliminary results of VLF signal perturbations produced due to solar flare. The data were recorded by the Stanford VLF AWESOME receiver located at National University of Ma-laysia, Selangor. Two new long distance (>1000 km) VLF paths, JJI-UKM (2700 km) and NWC-UKM (3300 km) were analyzed simultaneously. Data from the GOES satellite were used to determine the onset time and type of each of these flares. Results indicated that all five solar flare events with an X-ray peak flux above 10-5 W/m2 (M-class) were recorded, 37.5% for X-ray flux greater than 10-6 W/m2 (C-class), while the weakest X-ray flux recorded was 2.6 × 10-7 W/m2 (B-class) with 0.24% probing potentiality. We found a strong positive correlation (0.84) between solar flare radiation intensity and the values of amplitude and phase perturbations for both paths. The values of amplitude and phase perturbations time-correlated with solar flare, varied from 0.2 to 5 dB and 0.15 to 20 degree respectively. These findings are in complete agreement with previous works and demonstrate that the data obtained by the UKM AWESOME observation station will provide addi-tional contribution to the study of ELF/VLF waves phenomena in the ionosphere/magnetosphere, especially at low latitudes region.

Beamforming techniques for massive MIMO systems in 5G:overview,classification,and trends for future research

Massive multiple-input multiple-output(MIMO) systems combined with beamforming antenna array technologies are expected to play a key role in next-generation wireless communication systems(5G), which will be deployed in 2020 and beyond. The main objective of this review paper is to discuss the state-of-the-art research on the most favourable types of beamforming techniques that can be deployed in massive MIMO systems and to clarify the importance of beamforming techniques in massive MIMO systems for eliminating and resolving the many technical hitches that massive MIMO system implementation faces. Classifications of optimal beamforming techniques that are used in wireless communication systems are reviewed in detail to determine which techniques are more suitable for deployment in massive MIMO systems to improve system throughput and reduce intra-and inter-cell interference. To overcome the limitations in the literature, we have suggested an optimal beamforming technique that can provide the highest performance in massive MIMO systems, satisfying the requirements of next-generation wireless communication systems.