The demand for multimedia data services has grown up rapidly over the years. Fifth generation (5G) wireless communication which has been deployed in a lot of countries will not be able to meet up with users demand based on the estimated mobile traffic volume of users in the next few years. Therefore, the sixth generation (6G) communication has been proposed to address the constraints/limitations of 5G communication. 6G systems will operate in the millimeter wave (mmWave) and terahertz region which has larger bandwidth and offers higher data rate than 5G systems. 6G has a lot of opportunities like high-definition imaging, frequency spectroscopy, accurate localization and sensing, which is the main focus of this work. There are a lot of opportunities for sensing at higher frequencies because of fine resolution in all physical dimensions (range, Doppler and angle). Moreover, localization and sensing in 6G is made possible by the key enablers technologies which are: the use of new radio band for communication, intelligent beam-space incorporation of intelligent surfaces, artificial intelligence and machine learning methods. In the new proposed 6G system, it is necessary for localization, sensing and communication to coexist, sharing the same time-frequency-spatial resources. In other to enable sharing, cooperation, coexistence and co-design, various mechanisms and approaches are needed in 6G communication systems.

We address the problem of crowd estimation in situations such as indoor events using anonymous and non-participatory CSI of WiFi Signals. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on statistical mechanics, and relying only on sets of machine learning techniques.In this paper, a framework for crowd count estimation is presented which utilizes Chebyshev filter and SVD to remove background noise in the CSI data, PCA to reduce the dimensionality of the CSI data and spectral descriptors for feature extraction. From the extracted feature, a set of classiffying algorithms are then utilised for training and testing the accuracy of our crowd estimation framework The aim of this framework to effectively and efficiently extract the channel information in WiFi signals across OFDM carriers reflected by the presence of human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed framework. Our result depict that our estimation becomes more–rather than less–accurate when the crowd count increases.

We consider the problem of performing ranging measurements between a source and multiple receivers efficiently and accurately, as required by distance-based wireless localization systems. To this end, a new multipoint ranging algorithm is proposed, which is obtained by adapting superresolution techniques to the ranging problem, using for the sake of illustration the specific cases of time of arrival (ToA) and phase-difference of arrival (PDoA), unified under the same mathematical framework. The resulting nonparametric algorithm handles multipoint ranging in an efficient manner by employing an orthogonalized nonuniform sampling scheme optimized via Golomb rulers. Since the approach requires the design of mutually orthogonal sets of Golomb rulers with equivalent properties-a problem that founds no solution in current literature-a new genetic algorithm to accomplish this task is presented, which is also found to outperform the best known alternative when used to generate a single ruler. Finally, a Cramér-Rao lower bound (CRLB) analysis of the overall optimized multipoint ranging solution is performed, which together with a comparison against simulation results validates the proposed techniques.

The Deep Space Antennas are essential in achieving communication over very large distances. However, the pointing accuracy of this antenna needs to be as precise as possible to enable effective communication with the satellite. Therefore, in this research, the pointing accuracy for a Deep Space Antenna using Fuzzy logic control technique is addressed. In this work, a fuzzy logic controller for the azimuth position control for deep space antenna was designed and simulated using Simulink and MATLAB. Furthermore, the result was compared to that of a PID controller for the same system and it shows that the fuzzy controller has a relatively better performance with 0 percent overshoot, a settling time of 7. 4146s, and a steady-state error of 0.004358. Therefore, the antenna will be able to track the satellite with ±0.004358 pointing accuracy.