This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seamless mobility and efficient resource allocation. Traditional handover and load balancing techniques, primarily designed for 4G systems, are no longer sufficient to address the complexity of heterogeneous network environments that incorporate millimeter-wave communication, Internet of Things (IoT) devices, and unmanned aerial vehicles (UAVs). The review focuses on how recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are being applied to improve predictive handover decisions and enable real-time, adaptive load distribution. AI-driven solutions can significantly reduce handover failures, latency, and network congestion, while improving overall user experience and quality of service (QoS). This paper surveys state-of-the-art research on these techniques, categorizing them according to their application domains and evaluating their performance benefits and limitations. Furthermore, the paper discusses the integration of intelligent handover and load balancing methods in smart city scenarios, where ultra-dense networks must support diverse services with high reliability and low latency. Key research gaps are also identified, including the need for standardized datasets, energy-efficient AI models, and context-aware mobility strategies. Overall, this review aims to guide future research and development in designing robust, AI-assisted mobility and resource management frameworks for next-generation wireless systems.

We always face water leakage problems in underground distribution water networks (DWNs). Existing leak detectors suffer from false alarms due to poor leak signal quality affected by external noise, often collected by acoustic or vibratory sensors. This paper introduces a novel Discrete Wavelet Transform Detector (DWTD) that leverages precise pressure signals non-influenced by environmental noise. Using a prototype of a 100m PEHD pipeline and a diameter of 40mm, Data from two pressure transmitters were collected using a dSPACE MicroLabBox unit. The main idea is to apply the Discrete Wavelet Transform (DWT) with a DONOHO threshold law to cancel noises due to water turbulence fluctuations, ensuring high-quality signals for accurate leak detection and localization. As benchmarks to assess the quality of denoising signals three parameters were calculated, Signal to Noise Ratio (SNR 26.6763 dB), Normalized Cross-Correlation (NCC≈1), and Mean Square Error (0.20573 MSE 48.4761). The denoised temporal signals are obtained from the Inverse Discrete Wavelet Transform (IDWT). A Cross-correlation is employed to these signals to determine the leak’s location. The experimental validation involves positioning the first and second transmitters at specific distances on both sides of the leak position. This allows for comparison between the actual leak position in advance known and calculated positions at various points and leak sizes. With only a few exceptions where the maximum error rate reached 5 meters from the actual leak position, the detector's effectiveness was proven across tests involving four different leak sizes.