EMF Exposure Reduction Using Weighted Angle Model for Multi-Technology Sectorized BS

Complementing macro-only cellular networks with low-powered base stations is a promising deployment solution to improve both network coverage and capacity, and cope with exploding data traffic in the coming years. In Beyond 3G Networks, such as LTE-Advanced, Relay Nodes and micro base stations can transmit on the same spectrum as the overlaying macro layer, and guarantee higher spatial reuse through cell splitting. Differently from previous research studies, this paper specifically aims at evaluating and comparing the potential of LTE relay and micro deployment in a realistic metropolitan scenario. A heuristic deployment algorithm which combines network coverage and realistic spatial user density information is also proposed. The results show that for the downlink, in-band relays can be deployed to improve network coverage, but not substantially the network capacity due to the limitation of the wireless backhaul link. In-band micro deployment, on the other hand, is the best solution to boost downlink network capacity (up to 5 times), while also providing full network coverage.

Differentiated quality-of-Service (QoS) techniques are widely used to distinguish between different service classes and prioritize service needs in mobile networks. Mobile Network Operators (MNOs) utilize QoS techniques to develop strategies that are supported by the mobile network infrastructure. However, QoS deployment strategy can ensure that the radio resources provided by the base station are easily consumed if it is not used correctly or when different techniques are used all together. In this paper, we propose a scheduling algorithm and compare two different QoS deployment strategies for prioritized User Equipment (UEs) (with higher scheduling rates and dedicated bandwidth) that MNOs can use in the current infrastructure, using a commercial real-time Long Term Evolution (LTE) network in different test scenarios. Moreover, we expose the real-time user experience in terms of uplink throughput and analyze results of the UE’s real-time key performance indicators (KPIs) in detail. Experiment results are evaluated considering the implications of different QoS support types on network coverage and capacity planning optimizations. Our results demonstrate that even though pre-configured resource allocations can be given to prioritize UEs, the experience of all the UEs can be affected unexpectedly in the presence of many UEs who have received different QoS deployment support. Our experimental observations have revealed that location of the UEs with respect to Base Station (BS) and the availability of dedicated bandwidth UEs inside cell may have implications on the apriori defined resource allocation strategies of the other UEs.

This paper presents verifications for UMTS coverage and capacity in a live network using planning and performance tools applied to a real-world operational UMTS network. Case study results are presented and followed by comparisons between coverage predictions, capacity simulations, field measurements and system statistics. Tuned propagation models based on area clutter, population profiles, digital height maps and base station (BS) engineering elements are taken into account to match the outputs of the planning tool to the practical drive-test and UMTS Terrestrial Radio Access Network (UTRAN) system Key Performance Indicators (KPIs) measurements. This work outcome addresses the importance of using planning tools for dimensioning coverage and capacity aspects in UMTS networks and draw some attention to network future optimization, load balancing and congestions relief in advance before system traffic statistics are being generated or afterwards as supplementary grounds for network optimization and maintenance that support statistics.

Multi-hop heterogeneous cellular networks (MHCNs) consist of conventional macro cellular networks overlaid with an irregular deployment of low-power base stations (BSs), where the communication between BSs and mobile users can be established through a single hop or multiple hops. By modeling different kinds of randomly located BSs as K tiers of independent homogeneous Poisson Point Processes, we first explore the capacity of downlink MHCNs and derive the expression of capacity under Rayleigh fading channels. Particularly, the capacity gain achieved by cell splitting and multi-hop relaying is quantified for the first time. We then study the effects of BS density, transmit power, and signal-to-interference-plus-noise-ratio (SINR) threshold on the capacity of MHCNs. More importantly, we obtain the spectral efficiency enhancement condition under which the increase of BS density and transmit power improve the spectral efficiency, thereby enhancing the capacity. One interesting observation is that at a given SINR threshold, the capacity increases with BS density when all the tiers have the same SINR threshold. Moreover, the capacity of some special networks (i.e., heterogeneous cellular networks, multi-hop cellular networks, and conventional cellular networks) are derived directly by specializing some system parameters in our results. Finally, numerical studies and simulations are conducted to validate our analysis.

Wireless cellular networks are large-scale networks in which asymptotic capacity investigation is no longer a cliche. A substantial body of work has been carried out to improve the capacity of cellular networks by introducing ad hoc communications, resulting in the so-called multihop cellular networks. Most of the previous research allows ad hoc transmissions between certain source and destination pairs to alleviate base stations' relay burden. However, since reports show that Internet data traffic is becoming more and more dominant in cellular networks, we explore in this paper the capacity of multihop cellular networks with all traffic going through base stations and ad hoc transmissions only acting as relay. We first investigate the capacity of regular multihop cellular networks where both nodes and base stations are regularly placed. By fully exploiting the link rate variability, we find that multihop cellular networks can have higher per-node throughput than traditional cellular networks by a scaling factor of log 2 n. Then, for the first time we extend our study to the capacity of heterogeneous multihop cellular networks where nodes are distributed according to a general Inhomogeneous Poisson Process and base stations are randomly placed. We show that under certain conditions multihop cellular networks can also outperform traditional cellular networks by a scaling factor of log 2 n. Moreover, both throughput-fairness and bandwidth-fairness are considered as fairness constraints for both kinds of networks.

It is common knowledge that the transition of mobile networks from one generation to another is basically for the improvement in the network’s Quality of Service (QoS). Bearing this in mind, we will assumme that the Universal Mobile Telecommunication System (UMTS) will outperform the Global System for Mobile Communication (GSM), hence, the motivation to conduct this study in Calabar, Nigeria, for four mobile networks; MTN, Airtel, Globacom and 9mobile. With the aid of a TEMS investigation software installed in a laptop, a measurement campaign was carried out and log files collected, with focus on Call Setup Success Rate (CSSR), Dropped Call Rate (DCR), Handover Success Rate (HOSR), Call Setup Time (CST), network coverage and network quality. The collected data was analyzed with the aid of a TEMS discovery software. The analyzed data for each Key Performance Indicator (KPI) was compared with the minimum benchmark of the telecommunications regulatory body, the Nigerian Communication Commission (NCC). Result reveal that there was no outright improvement in the QoS and there was fluctuation in the QoS provided by the network operators. We therefore conclude that the network operators, either did not make accurate planning before installing their base stations or do not optimize their networks frequently and this led to poor QoS in most cases.

Breakthroughs in artificial intelligence, machine learning anddeep learning with great recent achievements have been strongly impacting the mobile communication industry. The traditional method ofmobile network management and operation has been shifting toward automation and proactive monitoring. Forecasting radio mobile networks’behaviors in coexisting multi-layer technology mobile networks for improving resource allocation efficiency and optimizing network’s key performance indicators becomes a critical task. This paper proposes usingLong-Short Term Memory (LSTM) network for predicting radio networks’ behaviors based on real statistical data from operational supportsystems. The promising results show the potential of our approach. Theprediction results can help mobile network operators leverage networks’operating performance.

In recent years, energy efficiency (“greenness”) has become an important research topic in wireless networks. Environmental awareness and the increased cost of energy stimulate the research on this subject. In cellular networks, most of the power consumption takes place at the base stations (BSs). It is worth noting that the number of BSs has been steadily increasing since the 1G networks; moreover, this increase is expected to become even steeper in the foreseeable future with the advent of the small cell concept in the envisioned 5G networks. It is also worth noting that in cellular networks the traffic demand (load) in space and time is getting increasingly heterogeneous. As a result, parts of network will likely be lightly loaded at certain times. In such situations, it only makes sense to switch off as many BSs as possible without jeopardizing the key performance indicators. Although there in an increasing volume of literature on the cell switch-off (CSO) concept, to the best of our knowledge, there is no study which considers the user terminal (UT) power consumption as a key performance indicator, while the UT power efficiency indeed constitutes one of most important performance criterion in a mobile network. In many cases, switching off BSs for downlink energy efficiency may result in an uplink energy inefficiency, due to the fact that the UTs served by the switched off BSs will need to be connected to further away BSs. In this paper, we propose a heuristic CSO algorithm to achieve energy efficiency in the whole cellular network while taking into account the power consumption of UTs. We call the proposed algorithm as the user-aware CSO algorithm.

This paper investigates the capacity of the cooperative cellular networks and proposes to use the 2-D nested deployment on the base stations. Further analysis of sum-rate capacity and spectrum efficiency shows that this approach provides significant improvement. The study leverages the traditional model of regarding the coverage of base stations as the hexagonal blocks and views each hexagon as a macro-cell. One of the theoretical contributions of this paper is to use the invariance in the difference co-array to cover the derive the entire set of the channel fading coefficients. Furthermore, this paper proves the validity of using the average sum-rate capacity to calculate the capacity of the networks in both Rayleigh fading and non-fading channels. We also use simulations to confirm the correctness of the proposed theories from another perspective. As a result, the proposed approach remarkably improves the capacity of the network with the same amount of base stations.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers’ view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

With the increasing network densification, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mmWave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mmWave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul.Technology trends for millimeter wave small cell field trails following 5G NR 3GPP standard has been noticed in the 5G NR Mobile era as an up gradation to LTE and LTE IOT services. 5G mobile will be fully cloud enabled services. This necessitates several pathways and gateways from IOT end nodes to access to the cloud via LTE/5G CRAN Multiservice Base station. This paper aims at design and development of 5G Network Emulator at SMIT Laboratory for experimental validation of the above concept.

The exponential growth in mobile data traffic, coupled with finite radio frequency resources, has intensified the challenges associated with efficient radio access network (RAN) planning and optimization in spectrum-constrained environments. As operators strive to meet increasing demands for high-speed connectivity and low-latency services, there is an urgent need for innovative strategies that can maximize spectral efficiency while ensuring quality of service (QoS) and user experience. This paper proposes a conceptual model for radio access planning and optimization specifically tailored to spectrum-limited settings, with a focus on dynamic resource allocation, interference management, and adaptive network configurations. The proposed model integrates multi-layered radio planning techniques, including spectrum reuse, frequency refarming, and heterogeneous network deployment, supported by real-time data analytics and artificial intelligence. The model introduces a feedback-driven architecture that continuously monitors network performance metrics—such as signal-to-interference-plus-noise ratio (SINR), throughput, and user mobility patterns—to guide adaptive reconfiguration of RAN parameters. Furthermore, it incorporates a spectrum-aware decision engine that enables efficient traffic offloading and load balancing across macro, micro, and small cells. To ensure robustness, the model accounts for regulatory constraints, spectrum licensing scenarios (licensed, unlicensed, and shared bands), and co-existence with legacy technologies. Emphasis is placed on the strategic placement of base stations, beamforming techniques, and interference coordination schemes such as enhanced Inter-Cell Interference Coordination (eICIC) and Coordinated Multipoint (CoMP). The framework also facilitates spectrum aggregation, dynamic carrier selection, and energy-efficient operations to enhance capacity without additional spectrum acquisition. This conceptual model serves as a valuable tool for mobile network operators, planners, and policymakers aiming to optimize network coverage and capacity under spectrum scarcity. It provides a scalable and technology-neutral blueprint applicable to 4G LTE, 5G NR, and emerging radio access technologies. Future work will focus on validating the model through simulations and real-world testbeds to refine its components and quantify its performance gains in diverse deployment scenarios.

We study the energy consumptions of two strategies that increase the capacity of an LTE network: (1) the deployment of redundant macro and micro base stations by the operator at locations where the traffic is high, and (2) the deployment of publicly accessible femto base stations by home users. Previous studies show the deployment of publicly accessible residential femto base stations is considerably more energy efficient; however, the results are proposed using an abstracted model of LTE networks, where the coverage constraint was neglected in the study, as well as some other important physical and traffic layer specifications of LTE networks. We study a realistic scenario where coverage is provided by a set of non-redundant macro-micro base stations and additional capacity is provided by redundant macro-micro base stations or by femto base stations. We quantify the energy consumption of macro-micro and femto deployment strategies by using a simulation of a plausible LTE deployment in a mid-size metropolitan area, based on data obtained from an operator and using detailed models of heterogeneous devices, traffic, and physical layers. The metrics of interest are operator-energy-consumption/total-energy-consumption per unit of network capacity.For the scenarios we studied, we observe the following: (1) There is no significant difference between operator energy consumption of femto and macro-micro deployment strategies. From the point of view of society, i.e. total energy consumption, macro-micro deployment is even more energy efficient in some cases. This differs from the previous findings, which compared the energy consumption of femto and macro-micro deployment strategies, and found that femto deployment is considerably more energy efficient. (2) The deployment of femto base stations has a positive effect on mobile-terminal energy consumption; however, it is not significant compared to the macro-micro deployment strategy. (3) The energy saving that could be obtained by making macro and micro base stations more energy proportional is much higher than that of femto deployment.

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type.

Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO) optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO) approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI) is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.