Macro Cell Research Articles

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

Energy-efficient Radio Resource Management (RRM) for 5G and beyond networks has become a key research challenge due to increasing Small Cells (SCs) densities and the high Quality of Experience (QoE) requirements of business users. Ensuring QoE and energy efficiency is essential in mobile networks, but these goals are often opposing and rarely addressed simultaneously in existing solutions. In this paper, we propose to include the QoE criterion in the RRM technique for 5G and beyond multi-layer networks, which will allow ordering individual QoE for business users. We developed a new radio resource allocation and optimization method to address changing user QoE requirements and reduce energy consumption in multi-layer 5G networks. The proposed method differs from the known ones in that it considers the QoE requirements of business users and load localization to optimally distribute the service process between Macro Cells (MCs) and SCs. This method uses a Voronoi diagram to energy-efficiently design the 5G Radio Access Network (RAN) by switching SCs to sleep mode when they are not serving active users. As a result, a balance is struck between user QoE requirements and network energy efficiency. Based on simulations, it is proved that the proposed method allowed more efficient use of accessible radio resources by 25% and reduced the energy consumption of the 5G RAN by 8.7% to provide the ordered QoE for users compared to traditional RRM methods.

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.

Resource allocation schemes in 5G: survey and challenges

In telecommunications, 5G is the fifth-generation of mobile networks with the enhanced features of higher data rates, lower power consumption, and extremely reduced latency over 4G. The spatial resource reused by 5G networks which is to facilitate customers with desired Quality of Service (QoS) by developing small cells into the coverage of macro cells. However, there is limited spectrum reuse and mutual interference among different users, an efficient Resource Allocation (RA) algorithm is required to reduce interference and to attain spectrum sharing. Many existing technologies have been proposed to solve some of the 5G challenges. In this paper, various resource allocation algorithms are analyzed and identify the challenging issues on where to focus attention for future research work.

Exploring the alternatives to the conventional interference mitigation schemes for 5G wireless cellular communication network

SummaryThe manifold increase in the number of cellular wireless network consumers has presented a monumental challenge to upgrade the current infrastructure for imparting superior throughput and good quality of service. End‐users of the network will want super high‐speed data with very low latency, as well as secure and with a lower packet data drop rate. Joint coordination between various network resources such as that of a dense heterogeneous network is crucial for achieving the above demands. It is also inevitable that such a dense arrangement of small cells or macro cells or other network equipment will be challenged by different forms of interference apart from being costly and less mobile. Beamforming is among the most advanced technology that is available up to date which has shown good results at handling interference and at guiding transmitted data to the desired end‐user. It is quite useful but also struggles from certain factors when deployed in a dense network. A thorough review of recent works of literature is detailed in the current paper with a focus placed on two major fields, which are interference mitigation techniques and gainful interference exploitation techniques. Some highly advanced interference mitigation techniques which are either developed or under development with their merits have been proposed. Most of these techniques use machine learning and artificial intelligence‐based methods to ensure the best quality of service (QoS) to every individual connected to the network. However some research works focused on negating away any kind of interference present in a signal, some focused on harnessing interference for optimizing the power of transmitted signals.

Hierarchical and Hybrid Cell Load Balancing in 5G Heterogeneous Mobile Networks

SummaryIn Fifth Generation (5G) Heterogeneous Mobile Networks (HetNets), deploying dense small cell networks makes user association more challenging. The process of collecting cell load information from the User Equipments (UEs) and broadcasting the feedback message involves significant overhead and time complexity. Moreover, the UEs may not know the optimum cell to reselect, satisfying its data rate requirements. In order to overcome these drawbacks, in this paper, we propose to design an Hierarchical and Hybrid Cell Load Balancing (HHCLB) technique using Selective Handoff. In this technique, the UEs of each cell are grouped into clusters depending on their proximity distance. Each cluster contains a cluster controller (CC) which is in charge of determining the intra‐cell load and redirecting the cell‐reselection request of a UE. If the data rate of any UE in a cluster becomes less than its required rate, then the cell reselection process is performed. By simulation results, it is shown that load balancing can be done proactively (implicitly) by the CCs when the load is unbalanced or can be done on demand (explicitly) when a UE send a request for cell reselection. In the case of Macro cells, HHCLB attains 71% higher throughput for low load scenario and 59% higher throughput for high load scenario. Similarly, in the case of Femto cells, HHCLB attains 19% higher throughput for low load scenario and 27% higher throughput for high load scenario.

Nature enthused high isolation high gain miniaturized multiple‐input multiple‐output antenna for A‐LTE/5G macro cell base transceiver station applications

Nature inspired high gain ±45dual polarized Ixora coccinea petal shaped massive multiple input multiple output antenna element is designed and developed with special feeding mechanism operating for 3rd Generation Partnership Project long term evaluation-time division duplexing mode for (2496–2690 MHz) and Sub-6 GHz of 5G Band 42 (3.4–3.6 GHz)/Band 43 (3.6–3.8 GHz) for macro cell communications are explored. Dual slant polarization is achieved by orienting two such petal shaped dipole antennas in ±45°. To validate the proposed antenna, the prototype is manufactured and measured. The antenna provides the bandwidth of 41.26% (2.5 to 3.8 GHz) with S11 < −15 dB, and port-to-port isolation>40 dB. Simultaneous operation of 4G and 5G with unidirectional stable radiation is achieved with broad-beam width of 73° ± 1.5° in the ±45° planes and gain varies from 10 to 11 dBi over the operating band. Due to its full planar structure the proposed antenna is easy to be fabricated.

RL-Based Waveform Adaptation With Partial Overlapping Tones in HetNets

Partially-overlapping tones (POT) mitigate the co-channel interference in a wireless network by exploiting the space between adjacent subcarriers through intentional frequency offsets (FOs). In this letter, we use partially-overlapping tones (POT) in a two-tier heterogeneous network, where multiple small cells (SCs) interfere with a macro cell. We propose a multi-agent Q-learning-based approach to obtain transmit power and intentional FOs assigned to SCs for filtered multi-tones with various pulse shapes. We show that the proposed method reduces the total interference and in turn, increases the throughput in the network. We then compare the performance of the proposed approach to the existing schemes and demonstrate its advantage with numerical results.

Interference Suppression and Resource Allocation Strategies Based on IoT Monitoring

The present application scenarios of the Internet of Things (IoT) often require the equipment to be adaptable, the resource allocation to be efficient, and the signal monitoring and transmission to be effective. However, the existing algorithms cannot solve the problem of system capacity reduction caused by the mutual interference between regions in data rates. Aiming at effectively improving the performance of the IoT monitoring system and ensuring the fairness of each monitoring terminal, this paper attempts to explore interference suppression and resource allocation strategies based on IoT monitoring. First, the paper established an IoT monitoring network model, and elaborated on interference suppression strategies for inter-layer interferences of “Macro Base Station (BS) – Micro Cells” and “Micro BS – Macro Cells” and for intra-layer interference that include the interference between local monitoring networks and interference between terminals in local area networks; then, the paper proposed a sub-carrier resource allocation scheme for IoT monitoring system with multiple inputs and outputs and a water-filling strategy of system channel power; at last, experimental results verified the effectiveness of the proposed interference suppression and resource allocation algorithm.

Inter-cell interference mitigation using adaptive reduced power subframes in heterogeneous networks

With the remarkable impact and fast growth of the mobile networks, the mobile base stations have been increased too, especially in the high population areas. These base stations will be overloaded by users, for that reason the small cells (like pico cells) were introduced. However, the inter-cell interference will be high in this type of Heterogeneous networks. There are many solutions to mitigate this interference like the inter-cell interference coordination (ICIC), and then the further enhanced ICIC (Fe-ICIC) where the almost blank subframes are used to give priority to the (victim users). But it could be a waste of bandwidth due to the unused subframes. For that reason, in this paper, we proposed an adaptive reduced power subframe that reduces its power ratio according to the user’s signal-to-interference-plus-noise ratio (SINR) in order to get a better throughput and to mitigate the intercell interference. When the user is far from the cell, the case will be considered as an edge user and will get a higher priority to be served first. The results show that the throughput of all users in the macro cells and pico cell will be improved when applying the proposed scheme in term of throughput for the users and the cells.

Interference Mitigation and Power Minimization in 5G Heterogeneous Networks

Macro cells’ (MCs) densification with small cells (SCs) is one of the promising solutions to cope with the increasing demand for higher data rates in 5G heterogeneous networks (HetNets). Unfortunately, the interference that arises between these densely deployed SCs and their elevated power consumption have caused huge problems facing the 5G HetNets. In this paper, a new soft frequency reuse (SFR) scheme is proposed to minimize the interference and elevate the network throughput. The proposed scheme is based on on/off switching the SCs according to their interference contribution rate (ICR) values. It solves the interference problem of the densely deployed SCs by dividing the cell region into center and edge zones. Moreover, SCs on/off switching tackles the elevated power consumption problem and enhances the power efficiency of the 5G network. Furthermore, our paper tackles the irregular nature problem of 5G HetNets and compares between two different proposed shapes for the center zone of the SC: circular, and irregular shapes. Additionally, the optimum radius of the center zone, which maximizes the total system data rate, is obtained. The results show that the proposed scheme surpasses the traffic and the random on/off switching schemes, as it decreases the outage probability and enhances the total system data rate and power efficiency. Moreover, the results demonstrate the close performance of both the irregular and circular shapes for the center zone.

Energy-Efficient Ultra-Dense Network Using LSTM-based Deep Neural Networks

As a means to achieve thousand-fold throughput improvements of future wireless communications, ultra-dense network (UDN) where a large number of small cells are densely deployed on top of the macro cells has received great deal of attention in recent years. While UDN offers number of benefits, intensive deployment of small cells may pose a serious concern in the energy consumption. Over the years, to reduce the energy consumption of UDN, an approach that turns off the lightly loaded base stations (BSs) has been proposed. However, determining the proper on/off modes of BSs is a challenging problem due to the huge computational overhead and inefficiency caused by the delayed decision. An aim of this paper is to propose a deep neural network (DNN)-based framework to achieve reduction of energy consumption in UDN. By exploiting the long short-term memory (LSTM) to extract the temporally correlated features from the channel information and the feedforward network to make BS on/off mode decision, we can control the on/off modes of BSs, thereby achieving a considerable reduction of the cumulative energy consumption. From the extensive simulations, we demonstrate that the proposed technique is effective in reducing the energy consumption of UDN.

Novel method for quantifying cells on carriers and its demonstration during SARS-2 vaccine development.

The most effective way to prevent and control infectious disease outbreak is through vaccines. The increasing use of vaccines has elevated the need to establish new manufacturing strategies. One of the major approaches is cell-based production, which creates a need for high cell density to enable higher cell production levels. This has led to development of the technology of cell carriers, including micro and macro cell carriers. To follow the production process, quantifying the number of cells on these carriers is required, as well as the tracking of their viability and proliferation. However, owing to various carriers' unique structures, tracking the cell's is challenging using current traditional assays that were originally developed for monolayers of adherent cells. The current "gold standard" method is counting cell nuclei, separating cells from the carrier, staining with crystal violet, and visually counting under a microscope. This method is tedious and counts both live and dead cells. A few other techniques were developed but were specific to the carrier type and involved specialized equipment. In this study, we describe a broadly ranging method for counting cells on carriers that was developed and employed as part of the development of severe acute respiratory syndrome coronavirus 2 vaccine. The method is based on the Alamar blue dye, a well-known, common marker for cell activity, and was found to be successful in tracking cell adsorption, cell growth, and viability on carriers. No separation of the cells from the carriers is needed, nor is any specialized equipment; the method is simple and rapid and provides comprehensive details necessary for process control of viral vaccine production in cells. This method can be easily implemented in any of a number of cell-based processes and other unique platforms for measuring the growth of encapsulated cells.

An Opportunity Downlink Interference Alignment Algorithm Based on Punch Scheduling in Cognitive Heterogeneous Networks

For the hierarchical structure and frequency reuse of cognitive heterogeneous networks, it is inevitable to effectively deal with interference while gaining capacity. In this article, we consider the downlink cognitive heterogeneous network, in which a macro base station can serve a certain number of macro users, and each macro cell is composed of a certain number of micro cells. An opportunistic downlink interference alignment (ODIA) algorithm for cognitive heterogeneous networks based on puncture scheduling is proposed. First, we designed a pair of precoding and decoding matrices to eliminate co-layer interference. Second, in order to reduce the overhead required to transmit system state information(CSI), we proposed a user scheduling algorithm that not only can obtain a larger interference-free size, but also improvements in fairness, system capacity and overflow probability. The simulation results show that the proposed ODIA scheme is superior to the existing interference management algorithms in terms of interference, system capacity and fairness of system resource allocation.

RADIO FREQUENCY RADIATION DEVICES AND ANTENNAS MAY VIOLATE THE RIGHT TO LIFE PRINCIPLE

This article proposesthat the Right to Life Principle, dened as “every person hasthe right to a natural birth and legitimate survival and development into adulthoodwithout environmental or othersystematic injury to theirwell-being,”may be violated byRFRemissionsfromcell phones,Wi-Fi,macro cell phone base stations (MCPBSs), 5G/4G small cell antennas (SCAs), etc. in excess of the standards set by the Building Biology Institute. BBI standardsset1000µW/m2as anextreme anomaly;theprecisedivisionpointofharm/noharmbelow1000µW/m2is unknown. I review literature describing (1) the Right to Life Principle, (2) the attributes of non-ionizing radiation, and (3) proven injury from cell phones, WiFi, MCPBSs, 5G/4G SCAs, etc. to living organisms with 20 categories of illnesses and 58 references. Non-ionizing radiation isshown to place a force eld on negatively charged particles including electrons, neurons, and DNA, and exciting/energizing electrons with shifts to outer orbits with energy emission when they return to ground orbit thereby destabilizing atoms, molecules, cells and organs in the process of orbital shifts. RFR induced illnesses include sperm damage, fetus injury, irreversibility infertility, emotional and hyperactive disorders, cancer, damage to DNA, the immune system, blood brain barrier, and stem cells, increasesin oxidative stress and free radicals, and harm to those living lessthan 500 metersfrom MCPBSs. My recommendationsto reduce injury from RFR are based upon review of the literature, experience in metering residential property and MCPBSsfor RFR, avoiding the use of RFR emitting devices and accessto line-of-sight antennas, and legislative proposalsto show the dangers of RFR devices and antennas by,for example,requiringnotice tobuyers andlessees ofresidentialpropertyof powerdensitieswithinhousingunits.

Energy Efficient Downlink Resource Allocation in Cellular IoT Supported H-CRANs

The cloud computing supported heterogeneous cloud radio access network (H-CRAN) is one of the promising solutions to support cellular IoT devices with the legacy cellular systems. However, the dense deployment of small cells with fractional frequency reuse in orthogonal frequency division multiple access (OFDMA) based H-CRANs increases intra- and inter-cell interference, turning the resource allocation into a more challenging problem. In general, the macro cell users are considered as the legacy users, whereas the cellular IoT devices and small cell users share the macro cell users' resource blocks in an underlaid approach. In this paper, we investigate an underlaid approach of resource allocation for small and macro cell users to improve the energy efficiency (EE) in H-CRANs. The solution approaches are derived with the Dinkelbach, Lagrange multiplier and Alternating Direction Method of Multipliers (ADMM) methods by considering the maximum power, resource block allocation, fronthaul capacity and quality of service (QoS) constraints of macro cell users. A two-step energy efficient underlaid cellular IoT (UC-IoT) supported H-CRAN method is proposed and evaluated with the overlaid cellular IoT (OC-IoT) supported H-CRAN and underlaid H-CRAN without cellular IoT devices. The proposed method is evaluated in terms of energy efficiency and the Jain's fairness index, considering the effect of number of cellular IoT density in each small cell of the H-CRAN. The simulation results demonstrate the effectiveness of the proposed approach compared to earlier approaches.

User association and resource allocation in 5G (AURA-5G): A joint optimization framework

5G wireless networks will be extremely dense, given the projected increase in the number of users and access points (APs), as well as heterogeneous, given the different types of APs and the applications being accessed by the users. In such challenging environments, efficient mobility management (MM), and specifically user association, will be critical to assist the 5G networks in provisioning the Quality of Service (QoS) of diverse applications 5G targets to serve. Whilst determining the most suitable AP for the users, multiple constraints such as available backhaul capacity, link latency, etc., will need to be accommodated for. Hence, to provide an optimal user association solution, in this paper we present a joint optimization framework, namely AURA-5G. Under this framework we formulate our user association strategy as a Mixed Integer Linear Program (MILP) that aims to maximize the total sum rate of the network whilst optimizing the bandwidth assignment and access point selection. We analyze multiple active application profiles simultaneously, i.e. enhanced Mobile Broadband (eMBB) and massive Machine Type Communication (mMTC), in the network and study the performance of AURA-5G. Additionally, we provision a novel study on the multiple dual connectivity modes, wherein the user can be connected to either one macro cell and a possible small cell, or with any two favorable candidate access points. Utilizing the AURA-5G framework, we perform a novel comparative study of all the considered scenarios on the basis of total network throughput, performance against baseline scenario and system fairness. We show that the AURA-5G optimal solutions improve the performance of different network scenarios in terms of total network throughput (by 38×–690×) and system fairness as compared to the baseline scenario, which is a conventional user association solution. Further, we also present a fidelity analysis of the AURA-5G framework based on the backhaul utilization, latency compliance, convergence time distribution and solvability. And since, a given network cannot always guarantee to satisfy the future network loads and application constraints, we show that AURA-5G can be utilized by the operators/vendors to evaluate the myriad network re-dimensioning approaches for attaining a feasible and optimal solution. Henceforth, we then explore the possibility of network re-dimensioning and study its impact on system performance for scenarios where the performance of AURA-5G is severely impacted due to the extremely strict nature of the constraints imposed in the MILP.

Interference mitigation using antenna selection for the heterogeneous networks

A rapid increase in the wireless internet-based applications led to an enormous increase in wireless data rates. Intensification of future wireless networks faces a great challenge to meet such growing demand for payload data. A suggested solution that can be used to resolve this issue is to overlay small cell networks with macro cell networks to provide higher network capacity and better coverage. Small cell networks experience large interference from macro cell base stations (BSs) making data rates received by the small cell users not reliably. In this paper, an antenna selection scheme based on small cell user’s (SCU) channel gain is proposed. Whereas, the two tiers use the same network bandwidth resources; the macro BS selects a subset of antennas which has a minimum interfering effect to the SCU based on a pilot sent from SCU to macro cell. The proposed selection scheme has been compared with convex optimization antenna selection scheme. Simulation results show that the SCU data rates are significantly improved using proposed scheme. Execution time required for antenna selection is reduced significantly using the proposed scheme.

Caching Policies for Delay Minimization in Small Cell Networks With Coordinated Multi-Point Joint Transmissions

In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small cells are combined to offer better Quality of Service to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) joint transmissions are used to further improve performance. In this paper, we address the average delay minimization problem by first formulating it as a static optimization problem. Even though the problem is NP-hard we are able to solve it via an efficient algorithm that guarantees a 1/2-approximation ratio. We then proceed to propose two fully distributed and dynamic caching policies for the same problem. The first one asymptotically converges to the static optimal solution under the Independent Reference Model (IRM). The second one provides better results in practice under real (non-stationary) request processes. Our online policies outperform existing dynamic solutions that are PHY-unaware.

Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application

In this work, discrete Fourier transform (DFT)-based channel estimation is proposed in generalized frequency division multiplexing (GFDM) system. In the GFDM system, the subcarriers are non-orthogonal; therefore, the pilot symbols cannot be easily observed due to the interference from data symbols and noise. The proposed method can improve the channel estimation of least square (LS) method by eliminating channel impulse response outside the number of actual impulse response. First, the received signal is demodulated using zero forcing demodulator. Then, it is divided with transmitted pilot symbols to obtain channel response. Interpolation in frequency and time domains is conducted to acquire channel response for all GFDM blocks. Finally, the channel estimation algorithm using DFT is performed. The parameters of the system are adjusted so that they are suitable for tactile internet application. The channel model used is NYUSIM, which utilizes mmWave. Three scenarios in NYUSIM such as urban microcell, urban macro cell and rural macro cell are used and power delay profiles generated from NYUSIM simulator are employed in this system. The results show that mean square error (MSE) from DFT-based channel estimation gives substantial improvement for all scenarios. In addition, symbol error rate (SER) of DFT-based channel estimation provides a slight improvement of ~1.5 dB than LS channel estimation.

Energy-Efficient Resource Allocation for Ultra-Dense Licensed and Unlicensed Dual-Access Small Cell Networks

In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices (operating in unlicensed bands), we consider the Long-Term Evolution (LTE) operation in unlicensed bands based on Carrier Sense Adaptive Transmission (CSAT), in which 'ON' and 'OFF' duty cycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6 and 11 percent) and (8 and 18 percent) the round-robin and the spectrum-efficient schemes, respectively, for two different small cell scenarios. In addition, it is shown that for less channel state information (CSI) estimation errors ς = 0.02, the maximum performance degradation of the proposed scheme is reasonably small (5.5 percent) as compared to the perfect CSI.