Rapid increase in mobile data has raised the stakes on developing innovative new technologies and cellular topologies that can meet these demands in an energy efficient manner. One of the most interesting trends that will emerge from this cellular evolution is the femtocell networks. Femtocells are small, inexpensive, low power base stations that are generally consumer deployed, and are expected to significantly improve the coverage and capacity of the indoor users. Femtocell base stations (FBSs) have extensive auto configuration and self-optimization capability to enable simple plug-and-play deployment. The FBSs perform self-optimization function that continually adjusts the transmit power so the femtocell coverage does not leak into an outdoor area while sufficiently covering the indoor femtocell area. In this paper, different power control techniques in femtocell networks have been discussed and compared. The focus is on distributed power control techniques due to the decentralized nature of femtocell networks. The conclusion drawn from this review is that the distributed power control techniques using pilot power control schemes are simple and effective in optimizing the coverage of femtocells as well as reducing power consumption of the FBS. Furthermore a novel algorithm is still needed to perform power control optimization in femtocell networks.  Index Terms-heterogeneous networks, femtocell, power control Coverage range Up to 500m <100m <30m Backhaul X2 interface X2 interface Home broadband Access mode Open to all users Open to all users Closed subscriber group Deployment Outdoors Indoors or outdoors Indoors Installation By the operator By the operator By the user Cost Expensive Cheap Very cheap

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Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications. Drones can provide airborne communication in a variety of cases, including as Aerial Base Stations (ABSs) for ground users, relays to link isolated nodes, and mobile users in wireless networks. However, variables such as the drone’s free-space propagation behavior at high altitudes and its exposure to antenna sidelobes can contribute to radio environment alterations. These differences may render existing mobility models and techniques as inefficient for connected drone applications. Therefore, drone connections may experience significant issues due to limited power, packet loss, high network congestion, and/or high movement speeds. More issues, such as frequent handovers, may emerge due to erroneous transmissions from limited coverage areas in drone networks. Therefore, the deployments of drones in future mobile networks, including 5G and 6G networks, will face a critical technical issue related to mobility and handover processes due to the main differences in drones’ characterizations. Therefore, drone networks require more efficient mobility and handover techniques to continuously maintain stable and reliable connection. More advanced mobility techniques and system reconfiguration are essential, in addition to an alternative framework to handle data transmission. This paper reviews numerous studies on handover management for connected drones in mobile communication networks. The work contributes to providing a more focused review of drone networks, mobility management for drones, and related works in the literature. The main challenges facing the implementation of connected drones are highlighted, especially those related to mobility management, in more detail. The analysis and discussion of this study indicates that, by adopting intelligent handover schemes that utilizing machine learning, deep learning, and automatic robust processes, the handover problems and related issues can be reduced significantly as compared to traditional techniques.

The impact of atmospheric attenuation on wireless communication links is much more severe and complicated in tropical regions. That is due to the extreme temperatures, intense humidity, foliage and higher precipitation rain rates with large raindrop sizes. This paper investigates the propagation of the mmwaves at the 38 GHz link based on real measurement data collected from outdoor microcellular systems in Malaysia. The rainfall rate and received signal level have been measured simultaneously in 1-minute time intervals for one year over a 300 m path length. The rain attenuation distributions at different percentages of exceedance time have been compared with the modified distance factor of the ITU-R P.530-17 model. The average link availability calculated with the measured rain rates has been analysed. Additionally, the key propagation channel parameters such as the path loss, path loss exponent, Rician K-factor, root mean square, delay spread and received power have been investigated considering the rain attenuation. These propagation channel parameters have been analysed using MATLAB software and explained with the help of the latest NYUSIM channel model software package (Version 2.0). The analysis results have been classified considering rain attenuation, antenna setup, link distances, antenna height and antenna gain. The outcomes revealed that the rain fade predicted by applying the modified distance factor provides high consistency with the measured fade in Malaysia and several available measurements from different locations. The large-scale path loss model in the NYUSIM simulation result was around 126.23 dB by considering the rain attenuation effects on the 300m path length. This work shows that the NYUSIM channel model offers more accurate rendering results of path loss for omnidirectional and directional antenna transmissions without rain fade. This study proves that the ability to provide good coverage and ultra-reliable communication for outdoor and outdoor-to-indoor applications during rain in tropical regions must be sufficiently addressed.INDEX TERMS Millimetre-wave, propagation channel, large-scale parameters, distance factor of the ITU-R P.530-17 model, NYUSIM channel model, rain fade, tropical regions.

Manual irrigation is still widely used in agricultural field using traditional drip and can watering. However, traditional irrigation systems are inefficient and inexact, leading to either insufficient or excessive watering. Moreover, it is difficult for farmers to predict suitable quantities at the appropriate time. Manual monitoring of the crop field may also lead to human error and is potentially risky for rural areas. Farmers may also not be aware of intrusions if they are not on location. Therefore, this project is designed to develop a smart monitoring and automated irrigation system to provide not only efficient water consumption based on specific conditions, but also enables real-time monitoring of the environment. Furthermore, this system prevents damage to plants and reduces the likelihood of plant theft. This system uses NodeMCU ESP32 as a microcontroller that collects environmental data such as humidity, temperature, soil moisture levels from sensors. The NodeMCU is integrated with a relay and RTC module to irrigate plants at specific times and is also equipped with a passive infrared sensor to detect intruders near the crop-field. Upon detection, an ESP32 camera is used to automatically capture the current conditions and farmers will be subsequently notified. Warnings are also sent to farmers upon detection of unwanted circumstances such as extreme temperature, which could prevent instances of open burning. The utility of the developed prototype is evident in the way it automatically irrigates the crop field without human intervention. Farmers may monitor and manually control the irrigation process using an attached Android application. Additionally, they may manually activate a buzzer warn off any potential malicious actors.