Deployment Of Small Base Stations Research Articles

MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future of wireless communication is envisioned to offer pervasive network coverage, high data rates, and a wide spectrum of services while maintaining high mobility. High data rates, wide range of services, huge connectivity, capacity, and good geographic coverage are being provided by the ultra-dense deployment of small base stations (BSs) in heterogeneous wireless networks (HWN). But dense deployment of small BSs, high mobility, network heterogeneity, imbalanced traffic, and dynamic user preferences lead to frequent handover. Network overhead, excessive energy consumption, and a decrease in service quality and user satisfaction can be due to frequent handover. So, handover management is one of the crucial challenges in the implementation of 5G and beyond in HWNs for ensuring seamless connectivity, energy efficiency, and the required quality of services and experiences. The effectiveness of handover decisions in HWNs relies on the implementation of a suitable network selection mechanism. Multi-attribute decision-making (MADM) is being used to model and analyze appropriate network selection complexities by considering a broad spectrum of intricate and conflicting decision criteria for efficient handover decisions in HWN. This article extensively explores, compares, and analyzes vital MADM techniques utilized for modeling appropriate network selection strategies in terms of algorithmic strategies, cardinality, types and significance of decision attributes, and network utilities. This article also examines, analyzes, and recognizes the recent mobility management challenges and trends in utilizing MADM strategies to tackle network selection issues in high-speed HWNs.

Mobile association scheme based on auction algorithm in heterogeneous wireless networks

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59% .

ECCANS: Enhanced CRITIC-based Context-Aware Network Selection algorithm for 5G HetNet

The ability of mobility management to provide seamless communication and better quality of service (QoS) to the multiservice multimode mobile node (MMMN), which can run multiple services simultaneously during roaming in ultra-dense heterogeneous networks with different radio access technologies, is a major challenge in implementing 5G and beyond. Ultra-dense heterogeneous networks (HetNet), restrictions on multiservice multimode mobile terminals, and user mobility all lead to a number of serious problems, such as the ping-pong effect, unnecessary handovers, and decreasing cell edge spectral efficiency. The dense deployment of small base stations also causes a high probability of frequent handovers in HetNet, which has led to a significant escalation in energy consumption resulting from unnecessary signaling, increased network overhead, and higher energy usage by network equipment. This increased energy requirement may lead to potentially increase the emissions of greenhouse gases to meet the energy production needs. Several handover decision algorithms have been proposed, but most of them deal with only running a single service at a time on a mobile node. In this paper, authors propose a new network selection model for MMMN which may run multiple services simultaneously. This model uses context-aware information about the user’s preferences, characteristics, and needs of the services, limitations of mobile terminal, and network conditions of an ultra-dense HetNet to choose the best network. The fundamental principle of this model is based on the comprehensive network criteria weight computed by the integration of Enhanced CRiteria Importance Through Inter-criteria Correlation (CRITIC), fuzzy analytic hierarchy process (FAHP), and group decision-making (GDM) techniques and the service priority of running services. For computing the objective weight of criteria, a novel improved CRITIC method has been proposed. At the same time, sigmoid utility functions and linear utility functions have been used to figure out the value of network criteria for running multiple services on the mobile node. Finally, the best network from HetNet for MMMN is selected using the technique for order performance by similarity to ideal solution (TOPSIS) and threshold by computing a comprehensive utility value network criteria decision matrix and comprehensive network criteria weight. The simulation results show that the proposed model increases the selection probabilities of an appropriate network according to the service requirements and network conditions. It also reduces unnecessary handovers and improves QoS while satisfying users’ preferences, service requirements, and terminal constraints, in solving the MMMN network selection problem.

A novel power consumption optimization framework in 5G heterogeneous networks

The fifth-generation (5G) mobile networks have the capacity to handle the dynamic traffic demands of the user equipment (UE). One approach is the dense deployment of small base stations (s-BSs), which can control the dynamic traffic demands. We consider s-BSs which are interconnected through the mmWave backhaul (BH) link to transfer traffic from the s-BS to the core network through the macro base station (MBS). In this setting, the network power consumption is affected by the way UEs are connected to the base stations and the traffic routes through the BH links. The main objective of this paper is to minimize the power consumption of the heterogeneous network (HetNet) with the intelligent backhauling and s-BS/BH link sleeping (IBSBS) framework. The proposed framework provides the minimized power consumption in HetNets through UE association, backhauling, and sleep s-BS/BH links. The UE association and backhauling uses a heuristic function based intelligent backhauling algorithm to assess the minimum power consumption from the s-BS to the MBS while considering the power and capacity constraints of the network. The load sharing based s-BS sleeping algorithm dynamically changes the states (active/sleep) of the s-BSs according to their loads without compromising UE demands. Different distributions of UEs and different data rates over the HetNets architecture are considered for performance evaluation. The evaluation results of the proposed framework outperform the state-of-the-art algorithms in terms of network energy efficiency, power consumption, and the number of active s-BSs/BH links.

Optimizing the stochastic deployment of small base stations in an interleave division multiple access‐based heterogeneous cellular networks

SummaryThe use of small base stations (SBSs) to improve the throughput of cellular networks gave rise to the advent of heterogeneous cellular networks (HCNs). Still, the interleave division multiple access (IDMA) performance in sleep mode active HCNs has not been studied in the existing literature. This research examines the 24‐h throughput, spectral efficiency (SE), and energy efficiency (EE) of an IDMA‐based HCN and compares the result with orthogonal frequency division multiple access (OFDMA). An energy‐spectral‐efficiency (ESE) model of a two‐tier HCN was developed. A weighted sum modified particle swarm optimization (PSO) algorithm simultaneously maximized the SE and EE of the IDMA‐based HCN. The result obtained showed that the IDMA performs at least 68% better than the OFDMA on the throughput metric. The result also showed that the particle swarm optimization algorithm produced the Pareto optimal front at moderate traffic levels for all varied network parameters of SINR threshold, SBS density, and sleep mode technique. The IDMA‐based HCN can improve the throughput, SE, and EE via sleep mode techniques. Still, the combination of network parameters that simultaneously maximize the SE and EE is interference limited. In sleep mode, the performance of the HCN is better if the SBSs can adapt to spatial and temporal variations in network traffic.

Effectiveness of Handover Control Parameters on Handover Performance in 5G and beyond Mobile Networks

Mobility management is essential in mobile communication networks to provide a smooth connection during users’ mobility. Handover control parameters (HCPs), such as handover margin (HOM) and time-to-trigger (TTT), are major and essential factors in mobility management that must be defined carefully to make efficient handover (HO) procedures. Their impact becomes more critical with the deployment of fifth-generation (5G) mobile networks and beyond (B5G). This is due to the different characterizations of next mobile networks, such as the use of millimeter-wave (mmWave) bands, the ultradense deployment of small base stations (BSs), large mobile connection traffic growth, and other more critical factors. The case becomes more sensitive with the high mobility speed scenarios. This study proposes different HCP system settings to be investigated and analyzed over B5G networks. They will be investigated with various mobile speed scenarios to illustrate their impact on the network performance. Various key performance indicators (KPIs) are considered to evaluate and validate system performance, such as reference signal received power (RSRP), HO probability (HOP), HO ping-pong (HOPP), radio link failure (RLF), HO interruption time (HIT), and HO failure (HOF). Results show that the various system settings provide different and significant impacts on the performance of B5G networks. Furthermore, the setting of HCP1 obtained the best performance in RSRP and RLF with -69.7 dBm and 4.8%, respectively, while the optimum performance of HOPP, HIT, HOP, and HOF is achieved in the HCP6 setting with 0%, 0.02 ms, 0.05%, and 0%, respectively. Moreover, the overall outcome of all HCP settings is 54.94%. These results indicate a tradeoff between RLF and HOPP with various HCP settings in B5G mobile networks. Thus, the HCP system settings must be adjusted carefully considering other factors, such as mobile environment and use case.

A Multi-Attribute Handover Algorithm for QoS Enhancement in Ultra Dense Network

Ultra-dense networks (UDNs) have been known as an efficient method to satisfy tremendous traffic demands in future mobile networks. With high density deployment of small base stations (SBSs), mobile user equipments (UEs) will suffer frequent and unnecessary handovers (HOs) which severely degrade the quality of service (QoS) and/or quality of experience (QoE). To solve this problem, we propose a novel multi-attribute mobility management strategy according to link reliability level and throughput requirement. In particularly, the reference signal received quality (RSRQ) and speed of UE are taken into account for the link reliability index. Besides, the minimum throughput is considered for guaranteeing QoS requirement of users. The UE reconnects the target small cell with the highest priority, which are calculated according to link index, QoS index, and corresponding weights. The goal of proposed mobility management scheme is to improve HO performance and QoS of UEs. Extensive simulation experiences with different traffic types are evaluated and the simulation results indicate the effectiveness of our mobility management strategy in terms of throughput and HO delay.

Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

Soft Mobility: Transparent Handover with Zero Handover Failure in User‐Centric Networks

User‐centric network (UCN) is regarded as a promising candidate to approach the challenges of more radio link failures (RLFs) due to the ultradense deployment of small base stations (SBSs) and meet the requirements of ultrahigh throughput, ultrahigh reliability, and ultralow latency for the 6G system. In this paper, soft mobility is proposed for UCN with the split of control and user plane (C/U‐plane) and shared physical cell identifier (PCI) to achieve the goal of zero handover failure (HOF) probability, where transparent handover (HO) within a cell is realized with user configuration duplication and measurement enhancement. Specifically, the cell is composed of several SBSs around the user, where one anchor SBS is selected for controlling, and others act as slave SBSs for transmission with duplicated UE configuration from the anchor SBS. Based on the proposed architecture, the user measures downlink channel quality for cells and SBSs distinguishingly, via SS/PBCH Block (SSB) and channel‐state information‐reference signal (CSI‐RS), respectively, and then makes the HO decision. Results show that soft mobility can reduce the number of HOF by about 50% over the current system, and the HOF probability is lower than 1% for TTT = 40 ms and offset = −1 dB.

Enabling Soft Frequency Reuse and Stienen's Cell Partition in Two-Tier Heterogeneous Networks: Cell Deployment and Coverage Analysis

Heterogeneous cellular networks (HetNets) are one of the key enabling technologies for fifth generation (5 G) networks. In HetNets, the use of small base stations (SBSs) inside the coverage area of a macro base station (MBS) offers higher throughput and improved coverage. However, such multi-tier base station deployment introduces new challenges, e.g., (i) All users experience significant inter-cell interference (ICI) due to frequency reuse, (ii) SBS associated users experience severe MBS-interference due to higher MBS transmit power, and (iii) MBS coverage edge users receive lower signal-to-interference ratio (SIR) due to longer distances. To address the aforementioned challenges, this work proposes a framework, including a novel cell deployment strategy, which combines Stienen's model with soft frequency reuse (SFR) and the corresponding performance analysis. According to Stienen's cell based SBS deployment, SBSs are deployed in MBS coverage edge area to enhance downlink SIR. To further mitigate ICI and MBS-interference, SFR is employed along with Stienen's cell based SBS deployment. Based on stochastic geometry, we derive expressions for coverage probability with four different types of cell deployment and SFR employment combinations. Numerical results indicate that SFR-enabled Stienen's cell based SBS deployment leads to enhanced edge user coverage and, hence, improves network performance gain.

SMDP-based sleep policy for base stations in heterogeneous cellular networks

A dense heterogeneous cellular network can effectively increase the system capacity and enhance the network coverage. It is a key technology for the new generation of the mobile communication system. The dense deployment of small base stations not only improves the quality of network service, but also brings about a significant increase in network energy consumption. This paper mainly studies the energy efficiency optimization of the Macro-Femto heterogeneous cellular network. Considering the dynamic random changes of the access users in the network, the sleep process of the Femto Base Stations (FBSs) is modeled as a Semi-Markov Decision Process (SMDP) model in order to save the network energy consumption. And further, this paper gives the dynamic sleep algorithm of the FBS based on the value iteration. The simulation results show that the proposed SMDP-based adaptive sleep strategy of the FBS can effectively reduce the network energy consumption.

Capacity driven small cell deployment in heterogeneous cellular networks: Outage probability and rate coverage analysis

AbstractHeterogeneous cellular networks (HCNets) are one of the key enabling technologies to improve performance gain of future cellular networks. Stochastic geometry is considered a promising tool to model and analyze HCNets. Users and base stations (BSs) are generally distributed uniformly using a homogeneous Poisson point process (HPPP). The assumption of uniformly distributed users is not suitable in HCNets because of the existence of clustered users in hotspots. To consider the correlation between the users and BSs, deployment of small base stations in these areas are of great concern to increase the performance of HCNets. In this article, we assume the notion of mixed user distribution, wherein the network users are the superposition of clustered and uniform users, modeled through HPPP and Poisson cluster process, respectively. We evaluate outage probability and rate coverage of the proposed HCNet model. We compare the network performance of the proposed mixed user distribution model with the conventional uniformly distributed user model. The analytical results are validated using Monte‐Carlo simulations. Our results show that the proposed HCNet model of mixed user distribution outperforms the uniformly distributed user model in terms of outage probability and rate coverage.

Performance analysis of user-centric SBS deployment with load balancing in heterogeneous cellular networks: A Thomas cluster process approach

In conventional heterogeneous cellular networks (HCNets), the locations of user equipments (UEs) and base stations (BSs) are modeled randomly using two different homogeneous Poisson point processes (PPPs). However, this might not be a suitable assumption in case of UE distribution because UE density is not uniform everywhere in HCNets. Keeping in view the existence of nonuniform UEs, the small base stations (SBSs) are assumed to be deployed in the areas with high UE density, which results in correlation between UEs and BS locations. In this paper, we analyse the performance of HCNets with nonuniform UE deployment containing a union of clustered and uniform UE sets. The clustered UEs are considered to be modeled according to Thomas cluster process, and random UEs are assumed to be deployed via homogeneous PPP. The SBSs are considered to be deployed at the center of the UE clusters, which results in user-centric SBS deployment. We derive outage probability and rate coverage of the proposed model. Furthermore, to improve the network performance, the impact of association biasing is also assumed. Our results show that the user-centric SBS deployment outperforms the conventional HCNets model. Increase in association bias upto a certain value results in performance improvement of the proposed user-centric HCNet.

Proactive Uplink Interference Management for Nonuniform Heterogeneous Cellular Networks

In homogeneous cellular networks, fractional power control (FPC) is employed to partially compensate the path-loss and, hence, improve uplink ( $U_{L} $ ) signal-to-interference ratio (SIR). However, this scheme is less effective in heterogeneous cellular networks (HetNets) because: (i) except the typical user, all other users with variable $U_{L} $ transmit power (UTP) act as interferers, (ii) FPC leads to high UTP by edge users and, hence, more interference, and (iii) small base stations (SBSs)’ densification further increases network interferences. Leveraging FPC in HetNets, we propose nonuniform SBS deployment ( $\text {NU-SBS}_{\mathcal {D}} $ ) to reduce interference and, thus, increase network performance. According to our $\text {NU-SBS}_{\mathcal {D}} $ model, SBS deployment ( $\text {SBS}_{\mathcal {D}} $ ) near macro base station (MBS) is avoided, whereas MBS coverage edge area is enriched with ultra-dense $\text {SBS}_{\mathcal {D}} $ . $\text {NU-SBS}_{\mathcal {D}} $ model leads to: (i) better SIR reception of MBS coverage edge users, (ii) fewer $\text {SBS}_{\mathcal {D}} $ requirement, and (iii) better SBS coverage in the MBS coverage edge area. Moreover, to make a model more proactive, we also consider reverse frequency allocation (RFA) to further abate both $U_{L} $ and downlink $(D_{L}) $ interferences. The coverage probability expressions are derived for both uniform SBS deployment ( $\text {U-SBS}_{\mathcal {D}} $ ) and $\text {NU-SBS}_{\mathcal {D}} $ while using RFA and FPC. Through simulation and numerical results, we characterize coverage probability for different values of SIR threshold, path loss compensation factor, SBS density, users density, and the distance between the typical user and the associated base station. The proposed $\text {NU-SBS}_{\mathcal {D}} $ model along with RFA leads to reduced network interference as compared with $\text {U-SBS}_{\mathcal {D}} $ and, thus, leverages FPC in HetNets.

Millimeter-Wave Coordinated Beamforming Enabled Cooperative Network: A Stochastic Geometry Approach

Millimeter-wave (mmWave) and ultra-dense networks are two key technologies for the fifth-generation (5G) and beyond communication system. However, the ultra-dense deployment of small base stations (SBSs) might introduce severe interference to users that connect to SBSs. This paper analyzes the performance of 5G communication networks where the SBSs with coordinated beamforming, operating at mmWave frequency band and macro base stations (MBSs) operating at sub-6 GHz coexist. First, by utilizing a stochastic geometry approach, we obtain the cell association probability expressions in terms of different cell association biases, base station density ratios and probabilities of line of sight (LoS) link. Furthermore, we propose a clustering method to choose some SBSs to eliminate intra-cell interference. Then, we put forward an average distance from the Kth SBS to a user to obtain signal-to-interference-ratio (SINR) and rate coverage probability expressions. The simulation results validate the correctness of the expressions, and indicate that the optimal cardinality of coordinated SBSs increases with the density of SBSs. In addition, the relationship between the cluster size K and the average energy efficiency is obtained, which can be used to guide the coordination principle in 5G and beyond communication systems.

Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function

Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE's experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE's Signal-to-Interference-plus-Noise-Ratio (SINR), cells' load and UE's speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms.

Data driven optimisation of small cell depolyment

Increasingly popular online social networks contribute to the massive growth in mobile traffic. The authors find that social network data, which is easier to obtain than carrier data, can be used to optimise the deployment of small base station (SBS). In this Letter, the authors propose a framework based on analysing social network data to identify and cluster mobile traffic hot-spots by using the density-based spatial clustering of applications with noise algorithm. By comparing the clustered hot-spots with the existing macro base stations' locations, they identify where additional SBSs need to be deployed and show that such deployment can effectively improve the quality of service. The proposed simulation results show that the SBSs deployment around hot-spot has relatively better user received signal power on mobile communication network than random SBS deployment following the normal distribution or the Poisson distribution.

Uplink Performance Analysis of User- Centric Small Cell Aided Dense HCNets With Uplink-Downlink Decoupling

The deployment of low power small base stations (SBSs) in high density user areas enhances the performance of heterogeneous cellular networks (HCNets). Such user-centric SBS deployment (UCSD) is more appropriate as compared with the conventional uniformly deployed user equipment (UE) based HCNets, because it captures the correlation between UEs and SBSs. In the literature, the downlink (DL) performance of UCSD based HCNet (UC-HCNet) has been analyzed under maximum received signal power (MRSP) association strategy. In this paper, we develop and analyze the uplink (UL) performance of UC-HCNets with decoupled UL-DL association (DUDA) using stochastic geometery. We evaluate the total UL interference considering two special cases of Poisson cluster process (PCP), i.e., Matern cluster process (MCP) and Thomas cluster process (TCP), in UC-HCNet. The proposed DUDA scheme enhances the system performance gain by assuming dual connectivity of UE and BSs in the UL and DL directions. Furthermore, fractional power control (FPC) policy is considered to limit the UL power consumption. The system performance is evaluated in terms of outage probability and rate coverage. The numerical results are validated through simulations. Numerical results show that the performance improvement of DUDA in sparse scenario is higher as compared with dense scenario. Furthermore, it is investigated that the performance gain of DUDA is higher for larger UE variance and cluster radius in case of MCP and TCP, respectively.

Analysis of coverage-oriented small base station deployment in heterogeneous cellular networks

In heterogeneous cellular networks (HetNets), dense small base station deployment (SBSD) offers a scalable and low-cost mechanism to meet the fifth generation (5G) needs of ubiquitous coverage and high throughput. However, due to high transmit power of macro base station (MBS), small base station (SBS) users experience severe MBS interference (MBS-I), and additionally, SBS coverage is significantly reduced when it is located near MBS. Furthermore, MBS edge users (MBS-EUs) experience signal-to-interference ratio (SIR) degradation due to their distant locations. To overcome such limitations, we propose Stienen’s cell based coverage-oriented SBS deployment (CO-SBSD) model. According to Stienen’s model, SBSD is avoided near MBS due to strong SIR reception from MBS and limited SBS coverage. However, in MBS edge area, SBSD enhances MBS-EUs coverage. Due to locations and density dependencies, performance characterization under Stienen’s model remains a challenging task as compared with the traditional Poisson point process (PPP) based models. Nevertheless, we demonstrate that the performance can be approximated in a tractable manner. Furthermore, we use spectrally efficient and effective interference abating scheme known as reverse frequency allocation (RFA) in conjunction with CO- SBSD, to mitigate MBS-I. Based on stochastic geometry framework, expressions for coverage probabilities are derived. The proposed CO- SBSD results in enhanced coverage performance as opposed to uniform-SBSD model. Moreover, RFA compliment the network performance gain by abating interference. Analytical and simulation results show that the proposed CO-SBSD, using Stienen’s model along with RFA employment, outperforms all other methods.

Approaching the cell switch-off problem in 5G ultra-dense networks with dynamic multi-objective optimization

Energy efficiency is a major issue in the fifth generation (5G) of cellular networks as they require an ultra-dense deployment of small base stations (SBSs) to meet the forecasted traffic demands. Switching off cells is a widely recognized strategy to reduce the power consumption of these networks during off-peak conditions but, as times goes by, these demands change, thus requiring the activation or deactivation of a different set of cells that provide the users with a minimum QoS. In this context, the optimization problem of selecting which cells have to be switched on/off in each period of time has been approached from the dynamic multi-objective evolutionary (MOEA) domain, by proposing a novel restart method that enables the algorithms to react to changes in the traffic demands. The newly devised operator, named Adjacent Cell Restart (ACR), is based on exploiting the spatial continuity of the mobile users in the network. The experiments over a set of 9 ultra-dense networks with increasing densities of both users and base station has shown the enhanced capabilities of the ACR-enabled dynamic MOEAs to better approximate the newly induced the Pareto fronts in consecutive periods of time.