Distributed base station mobility strategy and load balancing in wireless networks

The ever-increasing number of mobile telecommunication base station as a result of increasing demand for broadband applications has raised a growing concern and worry over the health implications and safety of the radiations from these base stations by the resident of Ekiti State and Nigeria in Nigeria. Measurement of radio frequency was conducted in this research in order to study the electromagnetic field radiation level in Ekiti State Nigeria. Investigation was conducted with the four available mobile operators with the three sub-frequency band viz 900 MHz, 1800 MHz and 2100 MHz. The power density of radio frequency radiation was estimated through measurement with the aid of A 3-Axis RF Radiation Strength Meter TM-196 and Handheld Spectrum Analyzer Model NA-773, 144/430. The peak and average power density was computed using the method of theoretical calculation and the safety distance from the antennas were estimated. The measured and the calculated values were compared with the international commission on non-ionizing radiation protection (ICNIRP) standard for public and occupational exposure level. Results show that the radiations from the base stations adheres to the standard provided by ICNIRP

This paper presents measurement and evaluation of radiation power density emitted from mobile cellular base stations in Abuja and it’s environ. Forty (40) mobile base stations with ten (10) each from AIRTEL, ETISALAT, GLO and MTN located in areas with high population were randomly examined across Abuja. Broadband analysis method was employed in the measurement of the radiation power density. The results obtained shows that most of the base stations recorded lower radiation power density below the safety standard value of 4.5watt/m2 , 22.5 watt/m2 for 900MHz frequency though few base stations recorded above standard value: GLO-BS02, GLO-BS03 and MTN-BS02 recorded 59.02 mW/m2, 39.76 mW/m2 and 46.70 mW/m2 respectively. The result also revealed that as distance increases, the mean power density reduces. This is to said that human exposure to radio frequency radiation have less or no health hazard at distance far away from the mobile cellular base station.

In traditional cellular networks with fixed base stations, the channel reuse pattern is static and deterministic. When the cell layout is dynamic, due to the mobility of base stations, the cluster of cells within cochannel interference range changes with time. Consequently, the channel reuse pattern is highly dynamic. Moreover, base stations also need wireless channels to communicate amongst themselves. A communication session between a pair of nodes may have to switch channels due to the movement of other nodes into the neighborhood. None of the existing dynamic channel allocation algorithms for cellular networks works in such a system. Hence, there is a need for new wireless channel allocation algorithms for virtual cellular networks with mobile base stations. In this paper, principles of mutual exclusion pertaining to distributed computing systems are employed to develop such an algorithm. The inter-base-station wireless links are referred to as backbone links, while the base station to mobile node links are referred to as short-hop links. The proposed algorithm is distributed, dynamic, and deadlock-free. Disjoint sets of channels are used for backbone and short-hop links. The distributed nature of the channel allocation scheme leads to scalability and robustness, as the responsibility is no longer centralized at the mobile telecommunications switching office (MTSO). Instead, it is shared among all the mobile base stations. In addition, the issue of channel rearrangement is addressed. Channel rearrangement is the switching of channels, performed to prevent cochannel interference, when mobile base stations using the same channels, hitherto not in interference range, come within the range of each other. If multiple channels are available to support a communication session, the channel selection policy can have a significant impact on performance. A random selection from the set of available channels yields better performance at low to moderate channel demand. This is in contrast to ordered channel selection (from one end of the spectrum), which always yields the best performance for cellular networks. Results obtained by simulating the algorithm are consistent with the theoretically obtained values.

A mobile computing system with no fixed nodes is presented. In traditional cellular systems with fixed base stations the channel reuse pattern is static and deterministic. When the cell layout is dynamic, due to the mobility of base stations, the cluster of cells within co-channel interference range changes with time. Consequently, the channel reuse pattern is highly dynamic, Moreover, base stations also need wireless channels to communicate amongst themselves. A communication session between a pair of nodes may have to switch channels due to the movement of other nodes into the neighborhood. Hence, there is a need for new wireless channel allocation algorithms for virtual cellular systems with mobile base stations. In this paper, principles of mutual exclusion pertaining to distributed computing systems are employed to develop such an algorithm. The inter-base station wireless links are referred to as backbone links while the base station-mobile node links are referred to as short-hop links. The proposed algorithm is distributed, dynamic and deadlock free. Disjoint sets of channels are used for backbone and short-hop links. The distributed nature of the channel allocation scheme leads to robustness as the responsibility is no longer centralized at the MTSO. Instead, it is shared among all the mobile base stations. This also makes the algorithm scalable.

Achieving maximum network throughput while providing fairness is one of the key challenges in wireless cellular networks. This goal is typically met when the load of base stations (BSs) is balanced. In a recent study, it was confirmed that the preamble sequence, which is mainly used for cell identification and synchronization, can be used as an implicit load indicator for long term load balancing in cellular networks. In this paper, we further explore its viability as an implicit information indicator, and present distributed load balancing schemes based on the properties of the preamble sequence. These schemes empower the mobile stations (MSs) to implicitly obtain information about the load status of BSs for intelligent cell-site selection. By utilizing the two characteristic and important properties of preamble sequences, which are high auto-correlation and low cross-correlation, we propose both network and user side algorithms that work together to enable the MSs to associate with BSs with relatively low load in the long term. We evaluate our algorithms for fairness to the MSs in terms of resources and throughput, and confirm through simulations that the preamble sequence can indeed be used as a leverage for better cell-site selection leading to a high degree of fairness in the system.

The aim of the present double-blind, sham-controlled, balanced randomized cross-over study was to disentangle effects of electromagnetic fields (EMF) and non-EMF effects of mobile phone base stations on objective and subjective sleep quality. In total 397 residents aged 18-81 years (50.9% female) from 10 German sites, where no mobile phone service was available, were exposed to sham and GSM (Global System for Mobile Communications, 900 MHz and 1,800 MHz) base station signals by an experimental base station while their sleep was monitored at their homes during 12 nights. Participants were randomly exposed to real (GSM) or sham exposure for five nights each. Individual measurement of EMF exposure, questionnaires on sleep disorders, overall sleep quality, attitude towards mobile communication, and on subjective sleep quality (morning and evening protocols) as well as objective sleep data (frontal EEG and EOG recordings) were gathered. Analysis of the subjective and objective sleep data did not reveal any significant differences between the real and sham condition. During sham exposure nights, objective and subjective sleep efficiency, wake after sleep onset, and subjective sleep latency were significantly worse in participants with concerns about possible health risks resulting from base stations than in participants who were not concerned. The study did not provide any evidence for short-term physiological effects of EMF emitted by mobile phone base stations on objective and subjective sleep quality. However, the results indicate that mobile phone base stations as such (not the electromagnetic fields) may have a significant negative impact on sleep quality.

Radio frequency exposure measurements in the surroundings of two mobile radio base stations were performed before and after their upgrade to 5G to investigate exposure changes. The measurements were carried out in an urban environment in Berlin, and a 5G Dynamic Spectrum Sharing (DSS) base station and a 5G massive MIMO base station were investigated. At the first base station, a previous UMTS system was replaced by a DSS system (LTE and 5G share common resources) with unchanged total transmission power. The maximum possible exposure at five out of six measurement points remains unchanged within the measurement uncertainty. At the second base station under investigation, a 5G massive MIMO antenna technology was additionally installed to an existing mobile radio system. Here, maximum possible exposure increases of 6 to 11 dB occur at the investigated measurement points. A parallel recording of the 5G instantaneous exposure at the massive MIMO station shows that the exposure without provoking traffic load (signalization only) and at low traffic load exploits only 5-10% of the maximum exposure in terms of field strength.

In the downlink of CDMA cellular systems, the traffic capacity is increased with fast closed-loop transmit power control (TPC) and soft handover. However, during soft handover, the balance of transmit power in the downlink is not maintained between base stations due to the reception errors of fast closed-loop TPC commands. The imbalance of transmit power results in the loss of traffic capacity. In order to recover the balance, adjustment loop TPC is proposed, in which the base stations gradually change the transmit power so that the difference of transmit power between base stations is reduced without disturbing fast closed-loop TPC. Computer simulations show that average transmit power in the downlink is reduced by 0.7 dB per mobile station by recovering the balance with adjustment loop TPC.

Background: There have been claims of a potential link between radio frequency (RF) emissions from mobile phone stations and cancer. In addition, widespread public concern has been expressed about the placement of cell phone antennas due to concerns about the risk of cancer. Objective: The objective of the study was to assess the effect of mobile phone base stations on oxidant and antioxidant markers and to investigate its potential correlation with cancer progression. Methods: The study involved three groups: cancer patients, healthy individuals residing near mobile phone base stations, and a control group living away from such base stations. Results: The study revealed significant differences in most biochemical parameters among the groups, highlighting the impact of mobile phone base stations on health. Cancer patients residing near mobile phone base stations (Group 1) showed notable changes compared to healthy individuals living both near (Group 2) and far (Group 3, control group) from mobile phone base stations. Specifically, the harmful effects of mobile phone base stations were evident in the increased total oxidant status (TOS), decreased total antioxidant capacity (T-AOC), and altered liver enzymatic activities (AST, ALP, ALT, LDH). Conclusion: This study finding suggests that proximity to mobile phone base stations may negatively influence oxidative stress and liver function leading to cancer. Further research is necessary to fully understand these effects and develop appropriate public health strategies.

Wireless mobile communication networks have become essential for human life. A great booming and huge investments are devoted to develop the services without taking into considerations the health effect of electromagnetic radiations of the base stations concerning with these services. Base stations for wireless mobile communication have brought Electromagnetic Field (EMF) sources to the permanent residences of people. A global increase in the level of electromagnetic background has taken place leading to its continuing presence in the environment. Cellular mobile radio communications have developed rapidly in Egypt and are one of the most actively growing sectors of the national economy. Many studies were made in this field and they did not prove that there is no effect for the EMF radiated from the mobile base stations. So, in this paper a procedure will be proposed to calculate the safety distance that the human beings can exposure from macro-cell of mobile base station. An on field measurement has been done to confirm this safety distance.

Energy management in wireless sensor networks is an important issue for the Internet of Things applications. Transmission power control (TPC) is one of the approaches to conserve energy in sensor networks. However, most existing TPC methods assume sensor nodes are stationary after deployment and thus do not support mobile sensing environments. In this paper, a predictive TPC approach, namely grey-fuzzy-logic-based TPC (grey-FTPC), has been proposed for mobile sensor networks using grey prediction and fuzzy inference systems. Simulation results indicate that the proposed approach not only reduces the energy consumption, but also maintains a proper packet delivery ratio (PDR) for mobile sensing environments. On average, the proposed grey-FTPC achieves 92.3% of the PDR of the conventional maximum transmission energy (MTE) approach, but consumes only 57.4% of the transmission energy of the MTE. Moreover, the grey-FTPC approach has an approximate performance compared with the Markov-based TPC, but does not require exhaustive experimental data to build models. Hence, it is believed that the proposed grey-FTPC approach could further be applied to highly mobile sensing environments.

Ultra dense network (UDN) is considered as a promising solution to support high throughput applications with lower operational cost. However, user mobility is even tricky for UDNs, especially in high-speed cases. While handover issues have been widely studied, the impact of high mobility on resource allocation needs to be further studied. In this paper, we investigate the impact of user mobility on transmit power control (TPC) and propose two kinds of mobility-aware TPC schemes which are adaptive to user's speed intelligently: mobility-aware constant TPC (MC-TPC), mobility-aware sub-slotted TPC (MS-TPC). Investigating the distance distribution between the moving user and its access base station (BS), we derive the analytical expressions of system outage probability (OP) and energy efficiency (EE) in the mobile environment to characterize the performance of the proposed and referenced TPC schemes. Numerical results show that the proposed mobility-aware TPCs significantly improve system reliability in terms of OP and data rate, especially with the proper sub-slots division. Meanwhile, in exchange for high reliability, EE degrades within a reasonable range with the increase of moving speed.

Unmanned aerial vehicles (UAVs) can serve as mobile base stations (MBSs) to provide wireless communication for ground terminals (GTs). This paper proposes a novel polynomial-time method to place MBSs, in order to minimize the number of MBSs ensuring each GT is within the wireless coverage of at least one MBS. The proposed algorithm transforms the deployment problem of MBSs into a minimum clique partition problem with the minimum enclosing circle coverage constraint. Based on the distance between GTs and the coverage radius of MBSs, the algorithm constructs an undirected graph $G(V,E)$ to denote the adjacent information between GTs. In our algorithm, the GT with the minimum degree is given a higher priority to deploy MBSs, and the location of each MBS will be refined gradually to cover as many as possible GTs. Numerical results show that, in the case where there are no capacity constraints for MBSs, the proposed algorithm performs advantageously over other algorithms in terms of the required number of MBSs as well as runtime overhead. Besides, we also analyze the impact of the capacity constraint of MBSs on the number of required MBSs, and compare the proposed algorithm with the Edge-prior algorithm on the case with the capacity constraint, showing that our algorithm requires fewer MBSs especially when the capacity of MBSs is high.

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

Mobile Backbone Wireless Networks (MBWN) [10] are wireless networks in which the base stations are mobile. Our strategy is the following: mobile nodes are dynamically grouped into clusters of bounded radius. In the very large wireless networks we deal with we deal with, several hundreds of clusters may be generated. Clustering makes use of a two dimensional Euclidean version of the Antipole Tree data structure [5]. This very effective structure was originally designed for finite sets of points in an arbitrary metric space to support efficient range searching. It requires only a linear number of pair-wise distance calculations among nodes. Mobile Base Stations occupy an approximate centroid of the clusters and are moved according to a fast practical bipartite matching algorithm which tries to minimize both total and maximum distance. We show that the best known computational geometry algorithms [1] become infeasible for our application when a high number of mobile base stations is required. On the other hand our proposed 8% average error solution requires O (k log k) running time instead of the approximatively O (k2) exact algorithm [1]. Communication among nodes is realized by a Clusterhead Gateway Switching Routing (CGSR) protocol [15] where the mobile base stations are organized in a suitable network. Other efficient clustering algorithms [11, 17] may be used instead of the Antipole Tree. However the nice hierarchical structure of the Antipole Tree makes it applicable to other types of mobile wireless (Ad-Hoc) and wired networks but this will be subject of future work.