Reference Signal Received Power Research Articles

Analysis of mobility robustness optimization in ultra-dense heterogeneous networks

Stable and reliable wireless connections during the user equipment's mobility are the critical issue in future mobile communication networks especially during UE's high-speed scenarios over dense heterogeneous networks (HetNets). Thus, setting an accurate value for the handover control parameters (HCPs) (i.e., time-to-trigger (TTT) and handover margin (HOM)) at different speed scenarios is required for system performance. This study proposes six different systems and each one employs different HCP settings using various mobile speed scenarios over a HetNet. Several performance metrics (i.e., received signal reference power (RSRP), handover ping-pong (HOPP), radio link failure (RLF), handover probability (HOP), handover interruption time (HOIT), and handover failure (HOF)) were used as key performance indicators (KPIs) for systems evaluations. Furthermore, 20 users were evaluated in this study using a 40 ms measurement interval. The results show a different impacts of each system on the performance of the deployed HetNet. However, System 1 (lowest TTT & moderate HOM) was the best system in term of RSRP and RLF but on the expense of HOPP. Furthermore, assigning high level of the TTT and HOM (i.e., TTT >1000 ms and HOM >8 dB) leads to 0 HOPP but high RLFs. Moreover, the best system performance in term of HOP, HOIT, and HOF were System 6 (highest TTT & high HOM) with 0.06 %, 3 %, and 0.004 %, respectively. System 4 represents the best trading off system between HOPP and RLF due to the proper configuration of the HCPs. However, the aims of evaluating several systems based on fixed HCPs over several mobile speed scenarios were to see the behaviors of the systems performance when different setting values are applied. Furthermore, this study proposes a HO self-optimization algorithm for auto-tuning the HCPs. Weighted function algorithm along with a trigger timer is used for reducing unnecessary HOs while RLF minimized by setting a trigger timer under a certain conditions when the RSRP of the serving base station (BS) goes below the received signal strength indicators (RSSI). The proposed algorithm shows a significant improvement in term of HOPP, RLF, HOP, and HOF compared to System 4 and FLC algorithm. The RSRP of the proposed algorithm is kept within acceptable range. Thus, accurate setting values for the HCPs are significant to keep the tradeoff between RLF and HOPP at the minimum level.

Handover management procedures for future generations mobile heterogeneous networks

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression.

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model's ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.

Hybrid cell handover strategy for O-RAN-based campus networks

In recent years, the concept of Open Radio Access Network (O-RAN) has become a prominent and ongoing research area in mobile communications, as mobile operators strive to enhance the intelligence, efficiency, and vendor independence of their RAN (Radio Access Network) architectures and components. O-RAN aims to virtualize and improve RAN functions in software and therefore it supports virtualized RANs where disaggregated network components of multiple vendors are connected via open interfaces. O-RAN is a concept based on interoperability and standardization of RAN elements including a unified interconnection standard for white-box hardware and open-source software elements from different vendors. Architecture integrates a modular base station software stack on off-the-shelf hardware which allows baseband and radio unit components from different suppliers to operate seamlessly together. Analyzing and examining the O-RAN architecture in terms of various network scenarios and different network policies is crucial for next-generation networks. This study begins by discussing the O-RAN architecture and its advantages in detail. Subsequently, a campus network has been proposed for the analysis and simulation of the O-RAN architecture, and performance analyses have been performed within the scope of different scenarios and network policies. As a result of these analysis, a new hybrid algorithm for cell handovers in the O-RAN architecture has been developed and compared with traditional cell handover methods based on Reference Signal Received Power (RSRP) and Quality of Service (QoS). The results indicate that the proposed hybrid handover algorithm for O-RAN architecture yields better outcomes.

Machine Learning for Wireless Network Throughput Prediction

This paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Power), which measures the power level of reference signals; RSRQ (Reference Signal Received Quality), an indicator of signal quality that provides insight into the number of users sharing the same resources; RSSI (Received Signal Strength Indicator), which gauges the total received power in a bandwidth; SINR (Signal to Interference plus Noise Ratio), a measure of signal quality considering both interference and noise; and other KPIs, all derived from three evolved node base stations (eNodeBs). After meticulous data cleaning, a subset of measurements from one serving eNB, spanning a 20-minute duration, was selected for deeper analysis. The PDCP DL Throughput, as a vital KPI metric, plays a paramount role in evaluating network quality and resource allocation strategies. Leveraging the high granularity of the data, the primary aim was to predict throughput. For this purpose, I compared the predictive capabilities of two machine learning models: Linear Regression and Random Forest. Metrics such as Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used to examine the models as they offer a comprehensive insight into the models’ accuracies. The comparative analysis highlighted the superior performance of the Random Forest model in predicting the PDCP DL Throughput. The insights derived from this research can potentially guide network engineers and data scientists in optimizing network performance, ensuring a seamless user experience. Furthermore, as the telecommunication industry advances towards the integration of 5G and beyond, the methodologies explored in this paper will be invaluable in addressing the increasingly complex challenges of future wireless networks.

Assessment of Output Radiation Density of Cell Phone for Epidemiological Studies: A Pilot Study in Navi Mumbai

In this research, the cell phone radiation output density of 167 different cell phone users has been systematically measured across various scenarios: (i) when no multimedia services were activated, (ii) when all multimedia services were activated (such as WhatsApp, Messenger, Viber, Skype, etc.), (iii) during calls without multimedia services, and (iv) during calls with all multimedia services activated. Through rigorous statistical analysis, the means for each of these four categories were computed. A notable finding is that the radiation output density from cell phones is at its highest during calls with all multimedia services activated (means 11.87 mW/m2 for 167 cell phones), whereas during calls with no multimedia services active, mean values are found 7.743 mW/m2. To establish a correlation between the quality of the signal received from the cell phone tower and the radiated power from the cell phone during calls, the output radiation from cell phones was also assessed using the Trifield Electromagnetic field (EMF) meter model (TF2) at different signal quality levels emitted by the towers. These signal quality levels were quantified by measuring Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Received Signal Signal-to-Interference-plus-Noise Ratio (RSSINR). It was observed that the cell phone's radiated power was at its lowest (means 9.428 mW/m2 for 41 cell phones) when the received signal quality was excellent (RSRP: –70.84 dBm; RSRQ: –12.06 Db; RSSINR: 12.6 dB). Conversely, in areas with the worst received signal quality (RSRP: –82.86 dBm; RSRQ: –17.034 dB; RSSINR: 8.1774 dB), the output radiated power density from cell phones during calls (all multimedia services activated) was recorded at its highest (means 18.336 mW/m2 for 56 cell phones). Furthermore, the study revealed an inverse correlation between the radiated output power density of cell phones (167) and the quality of the received signal (RSRPr: –0.18456; RSRQr: –0.35026; RSSINRr: –0.6448). This implies that the cell phone's radiated power density is directly influenced by the quality of the received signal from cell phone tower (specifically, its electromagnetic field strength) and the activation of various multimedia services. These findings contribute to improving the indicators of radiofrequency (RF) exposure for use in epidemiological studies. The results highlight that output power density of cell phone increases as the signal level from the cell phone tower decreases. Thus, we can affirm that a low-quality cell phone tower signal during calls leads to increased output radiation of cell phone, consequently resulting in a higher incidence of epidemiological problems.

Data-driven Analysis of the Cost-Performance Trade-off of Reconfigurable Intelligent Surfaces in a Production Network

This paper presents a comprehensive study on the deployment of Reconfigurable Intelligent Surfaces (RIS) in urban environments with poor radio coverage. We focus on the city of London, a large metropolis where radio network planning presents unique challenges due to diverse geographical and structural features. Using crowd-sourced datasets, we analyze the Reference Signal Received Power (RSRP) from end-user devices to understand the existing radio coverage landscape of a major Mobile Network Operator (MNO). Our study identifies areas with poor coverage and proposes the deployment of RIS to enhance signal strength and coverage. We selected a set of potential sites for RIS deployment and, combining data from the MNO, data extracted from a real RIS prototype, and a ray-tracing tool, we analyzed the gains of this novel technology with respect to deploying more conventional technologies in terms of RSRP, coverage, and cost-efficiency. To the best of our knowledge, this is the first data-driven analysis of the cost-efficiency of RIS technology in the production of urban networks. Our findings provide compelling evidence about the potential of RIS as a cost-efficient solution for enhancing radio coverage in complex urban mobile networks. More specifically, our results indicate that large-scale RIS technology, when applied in real-world urban mobile network scenarios, can achieve 72% of the coverage gains attainable by deploying additional cells with only 22% of their Total Cost of Ownership (TCO) over a 5-year timespan. Consequently, RIS technology offers around 3x higher cost-efficiency than other more conventional coverage-enhancing technologies.

Analisa Pengaruh Issue Overshooting Cell Terhadap Coverage dan Quality Jaringan 4G LTE Kelurahan Jati Baru Kota Padang

The increase in service users causes network problems, such as poor signal quality, sudden signal loss, or no network at all, To improve network quality, improvements are made to service coverage. One of them is by adding eNodeB. With the addition of eNodeB, network maintenance must be carried out to ensure identification and improvement related to network coverage and performance. If maintenance is not performed, it can cause bad spots, one of which is cell overshooting. The purpose of this research is to analyse the effect of cell overshooting on 4G LTE network coverage and quality parameters. 4G network coverage parameters are: Reference Signal Received Power (RSRP) and 4G network quality parameters namely Signal to Interference Noise Ratio (SINR). From the drive test results obtained 80.77% of 546 samples on RSRP parameters ≥ -100 dBm and 32.78% of 546 samples on SINR parameters ≥ 3 dB. The low SINR parameter of the drive test results is caused by bad spot overshooting cell interference that occurs at site 4253364E and site MC425CC02E with sites around the bad spot area. From the analysis, it can be seen that bad spot overshooting cell affects network quality, while network coverage parameters do not. Where the RSRP parameters at the bad spot point are -87 dBm and -92 dBm and SINR parameters at the bad spot point are -1 dB and -3 dB.

Roles of Wireless Networks in Bridging the Rural Smart Infrastructural Divide

The past decade has seen a rise in the availability of modern information and communication technologies (ICTs) for developing smart societies and communities. However, the smart divide, characterized by inequalities in ICT infrastructures, software access, and individual capabilities, remains a significant barrier for rural communities. Limited empirical studies exist that explore what and how ICT infrastructures can be developed to bridge the smart divide. The paper aimed to address rural broadband access in the context of infrastructural dimensions of smart divide (i.e., smart infrastructural divide) in the United States, focusing on the wireless network infrastructure’s role in narrowing the gap. It examined the broadband specifications needed for smart applications like smart education and telehealth, emphasizing the importance of wireless network capabilities. While fixed broadband offers higher speeds, wireless networks can support many smart applications with decent flexibility and ease of access. To further understand the implications of wireless broadband to rural communities, we conducted a case study in Carbondale and Cairo, two rural towns in Southern Illinois, using on-site user-inspired speed testing. An Android application was developed to measure download/upload speeds and Reference Signal Received Power (RSRP) for broadband quality. Results suggest both Carbondale and Cairo experienced below-average speeds with high variability among census blocks, which highlights the need for improved wireless network infrastructure. The paper culminated in the technological and policy recommendations to narrow down the smart infrastructural divide.

Novel Radio Resource Allocation Scheme in 5G and Future Sharing Network via Multi-Dimensional Collaboration

Radio resource allocation schemes are critical to enhance user experience and spectrum efficiency. In the context of fifth-generation (5G) and future networks, co-construction and sharing among multiple telecom operators, which effectively mitigate challenges stemming from resource scarcity, energy consumption, and network construction costs, also attract wide attention. Therefore, optimal resource allocation techniques in sharing networks should be explored. Current resource allocation schemes primarily optimize for load balancing, single-user throughput, and fairness of multi-user whole network throughput, with minimal consideration for network-level user experience. Moreover, existing approaches predominantly concentrate on specific resource domains, seldom considering holistic collaboration across all domains, which limits the user experience of the whole network. This paper introduces an innovative resource allocation method grounded in the Shannon theorem, incorporating time-frequency-spatial domain multi-dimensional collaboration. More importantly, by constructing an optimization model, we strive to attain optimal network-level user experience. Furthermore, we provide a smart grid technology based on the Artificial Intelligence (AI) method to predict inter-frequency information, including Received Signal Reference Power (RSRP), beam ID, and spectral efficiency, which are modeled as air interface utilization, channel bandwidth, and signal-to-noise ratio, respectively, providing input for the optimization algorithm, which seeks to achieve the optimal time-frequency-space resource allocation scheme. Extensive experimentation validates the effectiveness and superiority of our proposed methodology.

A 5G Coverage Calculation Optimization Algorithm Based on Multifrequency Collaboration

The fifth–generation (5G) network is developing rapidly. The network coverage directly influences the quality of service (QoS) of vertical industries. Coverage capability is a crucial and indispensable indicator when evaluating the performance of a network. However, the results of the current algorithm fall short in terms of accuracy. To restore the current status of 5G network coverage more realistically, in this study, we design a new optimization algorithm for coverage calculation based on the traditional coverage algorithm combined with fourth–generation (4G) coverage reference signal receiving power (RSRP) in management report (MR) and adopt a multisystem collaborative analysis method. The algorithm corrects the coverage results and restores the true value of 5G coverage. Based on this, we provide a practical analysis of the largest standalone (SA) commercial network in the world, which confirms the viability of the algorithm. Both theoretical and practical analyses show that the algorithm can effectively detect hidden weak coverage areas, providing a further reference for future 5G construction and improving the 5G user experience. The proposed approach can be broadly generalized and applied to multifrequency, multioperator, or even sixth–generation (6G) networks.

Distance‐based cell range extension and almost blank sub‐frame for load balancing and interference mitigation in 5G and beyond heterogeneous networks

AbstractThe goal of deploying LTE‐Advanced heterogeneous networks is to meet the growing need for Quality of Service (QoS), high data rates, and more coverage. Load balancing is one of the biggest problems, especially when User Equipment (UE) uses received signal strength to connect to different transmission power network tiers. Spectrum for small cells has not been used to its full potential. While all the cells are using the same frequency spectrum and the small cells overlap the Macro Cells, preventing interference is another big problem. The proposed Cell Range Extension (CRE) and Almost Blank Sub‐frame (ABS) provides a solution to overcome these challenges. The proposed ABS in this paper is novel because it is a partial muting one rather than the full muting ABS in recent research studies. In addition, the cell association method is by distance rather than conventional Reference Signal Received Power (RSRP). CRE‐ABS has been applied simultaneously to solve both load balancing and interference. Simulation results show that the proposed scheme achieves substantial throughput and Jain's Fairness Index (JFI).

Traffic based power consumption and node deployment in green LTE-A cellular networks

The energy utilization of green cellular networking or cellular network infrastructure has become a popular research domain due to the economic concern of network operators and global climate change. Also, due to the growth of wireless networks, the authors are attracted to finding novel relay station (RS) deployment techniques to support high user density and high data rate services by reducing energy consumption and operating costs of different network elements. The location of RSs greatly influences how far a cell can be covered. Therefore, the deployment RS plays a major role in widening the cell's coverage radius. To solve these challenges and to realize greener network designs by maximizing power consumption with QoS assurance, a novel traffic-based power consumption minimization and node deployment for the LTE green cellular networks is proposed. Also, key performance indicators (KPIs) are required to monitor and improve network performance. KPIs control the accomplished resource utilization and the quality of services. The research work comprises RS deployment, traffic estimation, and power allocation. Power losses, communication delays, increased implementation costs, and decreased throughput are the effects of an inappropriate RS deployment. Therefore, this research introduces the transmission area-based relay stations deployment scheme (TARSD) with multiobjective functions maximization of coverage area, minimization of the overlapping area and the minimization of power consumption cost. Traffic estimation helps to identify future capacity requirements and enables improved planning and decision-making. The deployment scheme of a cellular network relies on network traffic estimation. Nash traffic entropy learning algorithm (NTEL) is used to estimate traffic, and based on the estimated eNB switching ON/OFF is performed to minimize power consumption. Inter cell interference (ICI) can impact the performance of the LTE network. Several power allocation schemes can be used to improve system performance to achieve the optimal trade-off between interference and signal-to-noise ratio (SINR). Because of its good performance and efficacy in improving the mutual information between a channel's input and output, the enhanced low complexity water-filling (ELCWF) has been widely employed in power allocation with its increment and decrement properties. This research studies the statistical behaviour of a Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) and Signal to Interference noise Ratio (SINR). Also, statistical analysis of cost versus coverage performance is examined. Under the 7th simulation run, the proposed method yields the best coverage of 88.81 and 98.74%, respectively. In the experimental scenario, for the proposed model, the deployment cost for BS and RS are 27 and 13 units, the average throughput per user is 9.74 Mbps, and the coverage ratio is 97% under the BS candidate location of 20, respectively. In the power allocation stage, if the number of users is 50, the proposed model yields a power consumption of 0.2 W, throughput of 23.4 Mbps, delay of 15 ms and SINR of 20.12 dB, respectively.

Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication

SummaryDevice‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.

Two-stage clustering for improve indoor positioning accuracy

High accurate positioning, as a key factor for the most resource management algorithms, has attracted a lot of attention due to its crucial role in some new applications such as smart factories, IoT, and autonomous cars. Indoor positioning, as another major category of positioning in addition to outdoor positioning, is a very complicated process and achieving high accuracy is a tough process. This paper proposes a two-stage clustering-based approach (TSCA) for indoor positioning to deal with highly complex data sets which was collected from a large-scale indoor radio system. In the first stage, a new clustering algorithm, called group matching method is developed, which divides the dataset into several groups (sub-datasets) according to the different reference signal received power (RSRP) values of the real-world dataset. In the second stage, KNN and its variants, are used to match and evaluate the location of each device in one of sub-datasets instead of the entire dataset, which can increase the accuracy of the positioning. This method can perfectly solve the problem of uneven distribution of reference point data in the process of data acquisition, which is a popular challenge for most real scene data acquisition. The proposed method is compared with several state-of-the-art ML methods such as Support Vector Regression (SVR), and clustering methods such as K-means. The results indicate a high positioning accuracy improvement of more than 55% compared to a modified KNN method use our own RSRP fingerprint dataset, and a 2D accuracy improvement of 13.36% and a 3D accuracy improvement of 10.3% compared to a traditional KNN method use a Wi-Fi Received Signal Strength fingerprint.

Machine Learning Based Time Domain Millimeter-Wave Beam Prediction for 5G-Advanced and Beyond: Design, Analysis, and Over-The-Air Experiments

Artificial intelligence (AI) or machine learning (ML) based beam prediction is currently studied in the 3rd Generation Partnership Project (3GPP) fifth generation (5G)-Advanced new ratio (NR) standardization for future commercialization and standard evolution towards sixth generation (6G) communications, wherein time domain (TD) beam prediction is an important use case. The targets for such 3GPP studies and standardization are to lower power consumed at user equipment (UE) and reference signal (RS) overhead that are currently needed by frequent beam measurements due to UE rotation and mobility. To meet such targets, in this paper, we investigate AI/ML based algorithms facilitating TD beam prediction suitable for 5G-Advanced beam management (BM), including RS receive power (RSRP) prediction and beam change prediction. The proposed AI/ML algorithms are first evaluated through computer simulations with new UE mobility models based on recent standard evolutions in 3GPP. Then we further present over-the-air test results achieved by such AI/ML algorithms, using based station (BS) and UE compliant with 3GPP standards. Evaluation results show that the proposed schemes can accurately predict future beams and reduce large amount of the power consumed at the UE for BM, which also demonstrate feasibility of AI/ML based BM for 5G-Advanced and future 6G communications.

Automatic Clustering for Improved Radio Environment Maps in Distributed Applications

Wireless communication greatly contributes to the evolution of new technologies, such as the Internet of Things (IoT) and edge computing. The new generation networks, including 5G and 6G, provide several connectivity advantages for multiple applications, such as smart health systems and smart cities. Adopting wireless communication technologies in these applications is still challenging due to factors such as mobility and heterogeneity. Predicting accurate radio environment maps (REMs) is essential to facilitate connectivity and improve resource utilization. The construction of accurate REMs through the prediction of reference signal received power (RSRP) can be useful in densely distributed applications, such as smart cities. However, predicting an accurate RSRP in the applications can be complex due to intervention and mobility aspects. Given the fact that the propagation environments can be different in a specific area of interest, the estimation of a common path loss exponent for the entire area produces errors in the constructed REM. Hence, it is necessary to use automatic clustering to distinguish between different environments by grouping locations that exhibit similar propagation characteristics. This leads to better prediction of the propagation characteristics of other locations within the same cluster. Therefore, in this work, we propose using the Kriging technique, in conjunction with the automatic clustering approach, in order to improve the accuracy of RSRP prediction. In fact, we adopt K-means clustering (KMC) to enhance the path loss exponent estimation. We use a dataset to test the proposed model using a set of comparative studies. The results showed that the proposed approach provides significant RSRP prediction capabilities for constructing REM, with a gain of about 3.3 dB in terms of root mean square error compared to the case without clustering.

Adaptive cell selection algorithm for balancing cell loads in 5G heterogeneous networks

Heterogeneous networks (HetNets) are a promising solution for managing the exponential increase in the number of mobile users while maintaining high data rates and coverage. HetNets consist of various cell types with different cell coverage and system capacities. However, the traffic load in HetNets is variable, uneven and random over time, leading to unequal cell loads. Some cells have excessive user presence where the competition for system resources is high, while other cells have low user presence where system resources are not fully utilised. This paper proposes a mobility load balancing algorithm that prioritises millimetre-wave (mmWave) cells in target cell selection and user association to provide load balancing in 5G HetNets and improve overall system performance. A two-step target cell selection method that considers the load level of cells and reference signal received power (RSRP) is proposed. This method prioritises mmWave cells that meet cell load level and RSRP conditions in target cell selection, taking full advantage of the unique properties of mmWave cells (enhanced network throughput, spectral efficiency and enormous bandwidth) to balance traffic loads. The HO triggering and decision-making process is independently performed for each user. In case the serving cell is overloaded, different HO procedures are applied for load balancing depending on the target cell type (macro base station (BS) or mmWave BS). The scope of the study is further expanded by applying different HO procedures according to the serving and target cell types to maintain mobility robustness in case the serving cell is not overloaded. This research proposes various system scenarios with fixed HO margin (HOM) values and fixed time-to-trigger (TTT) durations to examine the effects of HCP settings on the proposed algorithm’s performance in terms of average load level of the serving cell, throughput, spectral efficiency and call dropping ratio (CDR). The system that provides the highest overall performance is applied to the proposed algorithm. To further assess and validate the performance of the proposed algorithm, it is compared with other load balancing algorithms from the literature. The simulation results reveal that the proposed algorithm does provide noticeable enhancements in network performance in terms of load level, throughput, spectral efficiency and CDR for various mobile speed scenarios as compared to existing load balancing algorithms.

Comparative Analysis of the Features of a 5G Network Production Dataset: The Machine Learning Approach

5G networks deployment is much data driven, leading to more energy consumption. The need to efficiently manage this energy consumption is a major drive in the comparative analysis of the features of a 5G production dataset. The features of the 5G production dataset generated with G-Net track pro were analyzed using Python programming language. From the correlation coefficient results obtained, the highest correlation value of 0.78 exists between the reference signal power and the received signal reference power of the neighbouring cells. Using the significant indicator, we observed that the signal to noise ratio is the most important of all the features. Using heat map and scatter plots, we further observed that there were good relationships between the key features selected from the significant indicator. These features will play a big role in improving the energy efficiency of a 5G network.

Beam tracking in phased array antenna based on the trajectory classification

AbstractOne of the major new concepts in 5G cellular communications is a shift in the way services are delivered to users. In the new 5G protocol, individual users are recognized and tracked through beams, which are targeted and specific to individual users. The millimeter‐wave (mmWave) communications impose a directionality which results in a significant challenge in serving mobile terminals and unmanned aerial vehicles. This challenge can be relieved in mmWave systems using analog beamforming. Based on the identification of the patterns in the reference signal received power (RSRP) measurements, some classifications are employed. Some trajectories are defined for different users in Hallways. Therefore, the angle of arrival (AoA) would be the same for the users following the same trajectory. The users based on the received RSRP values are clustered by K‐means and I‐Kmeans algorithms. To this end, our algorithms to find the beamforming coefficient are employed for only one user in each cluster and the complexity of the algorithms can be lower. The proposed algorithms are spatial frequency‐based beam tracking and angular domain beam tracking algorithms. Simulation results show that tracking the sine function of AoA achieves better performance compared to tracking the AoA. Moreover, the optimal number of clusters is obtained by using the Elbow method.