Coverage and Capacity Optimization Analysis in Heterogeneous Cellular Networks Based on Truncated Function

Ultra-dense Heterogeneous networks (UDHN) are emerging as the inevitable approach to cope with the imminent cellular network capacity crunch. However, load imbalance and widely disproportionate SINR distribution between macro and small cells, remains the key hurdle in harnessing the full potential of UDHN. In this paper we address this problem by proposing and analysing a novel load-aware user association methodology that offers a mechanism to simultaneously optimize network capacity, load distribution and coverage. The solution concurrently leverages the three key optimization parameters for Coverage and Capacity Optimization (CCO) and Load Balancing (LB) SON functions i.e. antenna tilts, transmit powers and cell individual offsets (CIOs). The method incorporates exponential-weighting based prioritization of CCO and LB SON functions within the user association process. The results suggest that the proposed approach offers a distribution of load between macro and small cells that yields more gain in terms of both network capacity and user quality of service than conventional max signal strength or max SINR based association methods.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) is a promising passive device that contributes to full-space coverage via transmitting and reflecting the incident signal simultaneously. As a new paradigm in wireless communications, how to analyze the coverage and capacity performance of STAR-RISs becomes essential but challenging. To solve the coverage and capacity optimization (CCO) problem in STAR-RIS-assisted networks, a multi-objective proximal policy optimization (MO-PPO) algorithm is proposed to handle long-term effects. To strike a balance between each objective, the MO-PPO algorithm provides a set of optimal solutions to approach a Pareto front (PF), where the solution on the approximate PF is regarded as an optimal result. Moreover, in order to improve the performance of the MO-PPO algorithm, two update strategies, i.e., action-value-based update strategy (AVUS) and loss function-based update strategy (LFUS), are investigated. For the AVUS, the improved point is to integrate the action values of both coverage and capacity and then update the loss function. For the LFUS, the improved point is only to assign dynamic weights for both loss functions of coverage and capacity, while the weights are calculated by a min-norm solver at every update. The numerical results demonstrated that the investigated update strategies outperform the fixed weights MO optimization algorithms in different cases, which include a different number of sample grids, the number of STAR-RISs, the number of elements in the STAR-RISs, and the size of STAR-RISs. Additionally, the STAR-RIS-assisted networks achieve better performance than conventional wireless networks without STAR-RISs. Moreover, with the same bandwidth, a millimetre wave is able to provide higher capacity than sub-6 GHz, but at a cost of smaller coverage.

The management and operation of a Self-Organizing Network (SON)-enabled mobile network still requires considerable human effort. On the one hand, SON Functions need to be configured through low-level parameters in order to control the optimization of the network. On the other hand, an operator wants to steer the system with solely technical objectives, and the underlying network should be adapted accordingly. This opens up a gap in network management that is currently closed manually. This paper presents an approach that overcomes the manual gap between technical objectives and SON Functions by choosing the best values for the SON Functions' configurations automatically. Main advantage of this approach is that it allows to manage a system at a high level of abstraction and, at the same time, reduces manual effort. The approach is explained by applying it in a case study in the field of mobile networks with four SON Functions, namely Mobility Load Balancing (MLB), Coverage and Capacity Optimization (CCO), Energy Savings Management (ESM) and Mobility Robustness Optimization (MRO).

The deployment, operation and maintenance of complex cellular networks are managed autonomously by multiple concurrently executing Self-Organizing Network (SON) functions with dedicated objectives, that can often negatively impact the functioning of each other. It is essential to avoid the blinkered view to their individual targets and consider a holistic approach towards identifying the best possible coordination between them, in order to achieve desired overall network gains while ensuring stable and robust network operation. The designing of appropriate SON-coordination mechanisms is quite challenging as it requires comprehensive modelling of all the complementing and conflicting interactions among them. This article discusses the application of Machine Learning based online Recommender Systems to model the dynamics between SON functions. To evaluate its applicability, in this work, the focus is to jointly implement two intertwined SON functions - Inter Cell Interference Coordination (ICIC) and Coverage and Capacity Optimization (CCO), to implicitly handle their conflicts and achieve the desired trade-off between coverage and capacity by optimizing a joint objective. The proposed cooperative learning and distributed configuration enforcement based ICIC-CCO coordinated SON solution has been evaluated on a system-level LTE network simulator with varied traffic distributions. It has been observed that the outage situations in the network are significantly reduced while still achieving high Signal-to-Interference Ratios (SIRs), even with reduced transmit power settings on several occasions.

In dense and heterogeneous networks, the decoupled uplink#x002F;downlink (UL#x002F;DL) access (DUDA) design has drawn great attentions for improving system performance. Energy efficiency (EE) becomes a major concern for densely deployed heterogeneous cellular networks (HetNets). In this paper, we theoretically analyze the energy efficient sleep strategy for DUDA HetNets. Through using stochastic geometry theory, we first examine the applicability of conventional sleep strategy to DUDA networks and design a new DUDA sleep strategy. We then formulate the energy consumption minimization problem and EE optimization problem, and derive the optimal BS sleep probability. Numerical results reveal that conventional sleep strategy may provide inaccurate guidance for sleep design in DUDA networks, which may lead to excessive sleeps and decrease system EE. Meanwhile our DUDA sleep strategy can effectively reduce network energy consumption. We also find that the dense deployment of small cells may generally increase network EE, but this improvement saturates as the BS density further increases.

The guard region for a randomly select user is introduced based on the stochastic geometry model. The guard region, through which a frequency reuse criterion is proposed, is calculated based on parameters such as user position, path loss exponents and transmission power. A dynamic frequency reuse scheme based on the guard regions of macrocell users is proposed to mitigate inter-layer interference. A frequency reuse criterion, which changes along with user position adaptively and is derived through stochastic geometry theory, is believed to be the first effort that can be found in the literature.

In this paper we introduce a mathematical model in the framework of long term evolution (LTE) to solve the problem of coverage and capacity optimization by employing multi-level random Taguchi's Method. The optimization process runs in a centralized manner with no human intervention required and interacts with environment situation automatically. Under the mathematical model which combines coverage and capacity optimization by a coefficient factor, conventional Taguchi's Method transcends traditional trial-and-error approach in convergence speed and simple implementation, to offer even more search capacity, Gaussian shrink coefficient and random optimization offset are applied to the level-to-tilts mapping function. Antenna tilt is an effective interference reduction technique, which has been adopted as the tuning parameter. The simulation results turn out to be better than traditional Taguchi's Method and trial-and-error approach. The system model presented and evaluated also gives an insight to the mathematical model especially the trade-off between coverage and capacity in homogeneous scenario.

Heterogeneous network (HetNet) has been studied as a promising technology to boost the system throughput. A new upper boundary of coverage probability (CP) is derived, which is the one of cooperative communication between two base stations (BSs) for downlink HetNet. For the tractability, only the cooperation between two base stations in the same tier of HetNet is considered and a model is set up, in which each randomly chosen user located in the cooperation zones can communicate with at most one couple of BSs, due to the threshold of Signal-to-Interference-Ratio (SIR). Stochastic geometry theory is used to establish the model of HetNet. Simulation results show that the upper boundary of CP for cooperative communication between two BSs in the same tier is decreasing when the SIR threshold for macrocell tier is increasing. The increasing density of low power BSs can increase the performance of cooperative communication in the same tier, as well as reducing the cooperative SIR threshold for low power BSs tier.

A key component of wireless sensor networks (WSN) is optimal coverage. For WSN to have network lifetime and optimal resource utilization, maximum coverage must be guaranteed. The unequal distribution of sensor nodes in densely populated regions contributes to the build-up of network coverage. This research suggests a revolutionary intelligent deep learning-based optimization approach for WSN coverage optimization to tackle this problem. Here, the optimal network coverage of WSN is performed by the novel enhanced deep Q-network (EDQN) algorithm, where the parameters of DQN are tuned by nature inspired optimization algorithm called hippopotamus optimization (HO) with the intention of attaining the fitness function. In order to maximize coverage, the node position is changed in the developed methodology to represent the spatial properties of the network. Along with lowering latency, the increased coverage also increases throughput and network longevity. The outcome of several network coverage optimization trials is computed, and the EDQN-HO’s impact on network coverage optimization is also determined by adjusting the parameters. The network coverage optimization studies’ simulated findings demonstrate that the suggested EDQN-HO may be effectively utilized in a variety of settings. The proposed EDQN-HO for the WSN coverage model returns superior outcomes with 18.31%, 78.95%, 10.65%, 87.5%, 83.33%, and 38.20% than the existing methods in terms of coverage rate, energy consumption, computing time, positioning error, network lifetime, and average moving distance respectively.

Millimeter wave (mmWave) is promising for the fifth generation cellular systems. The sensitivity of mmWave signals to blockages, such as buildings in cities, however, makes the network performance hard to predict. Using concepts from stochastic geometry and random shape theory, this paper proposes an analytical framework to incorporate the blockage effects and evaluate the performance of mmWave cellular networks, in terms of coverage probability and achievable rate. Leveraging prior work on a blockage model, a stochastic characterization of the regions covered by line-of-sight (LOS) and non-LOS links is obtained, which allows different path loss laws to be applied to the LOS and non-LOS links, respectively. Based on the proposed framework, analytical expressions for the mmWave downlink coverage probability are derived, and then the network performance is examined. Numerical results show that millimeter wave (mmWave) networks can provide comparable coverage probability and much higher data rates than microwave networks.

A K-tier heterogeneous downlink millimeter wave (mmWave) cellular network with user-centric small cell deployments is studied in this paper. In particular, we consider a heterogeneous network model with user equipments (UEs) being deployed according to a Poisson Cluster Process (PCP), i.e., Thomas cluster process, where the UEs are clustered around the base stations (BSs) and the distances between UEs and the BS are modeled as Gaussian distributed. In addition, distinguishing features of mmWave communications including directional beamforming and a sophisticated path loss model incorporating both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions, are taken into account. In this paper, the complementary cumulative distribution function (CCDF) and probability density function (PDF) of the path loss are provided. Also, using tools from stochastic geometry, we derive a general expression of the signal-to-interference-plus-noise ratio (SINR) coverage probability. Our results demonstrate that coverage probability can be improved by decreasing the size of UE clusters around BSs, and interference has noticeable influence on the coverage performance of our model.

One of the promising solutions to meet the demands for ubiquitous coverage and high capacity in heterogeneous cellular networks (HCNets) is the co-deployment of small base stations (sBSs) inside the coverage region of a macro base station BS. Due to its high transmit power, most of the users get associated with BS. This results in imbalanced load distribution across the HCNets. To balance the load, users from BS are offloaded to low power sBSs to decrease the BS load and increase network capacity. However, BS now acts as a strong interferer for offloaded users. Therefore, a proactive interference mitigation technique together with load balancing is required to mitigate the interference and balance the network load. In this paper, we use the soft frequency reuse (SFR) scheme to abate such interference. Furthermore, we use cell range expansion (CRE) based cell association where coverage regions of sBSs are increased by using cell association bias to include more users. Moreover, we derive coverage probability expressions for the proposed model. Numerical results demonstrate that the SFR scheme together with association bias reduce interference and balance network load distribution.

This paper evaluates the transmission rate and the delay of the uplink transmission and backhaul links in an aerial Wi-Fi network composing unmanned aerial vehicle (UAV) in emergency communications, where UAVs are equipped with buffer to relay the data to base station (BS) over the millimeter wave (mmw) backhaul links. The user equipments (UEs) connect to aerial Wi-Fi access points using Carrier Sense Multiple Access (CSMA) scheme in the uplink transmission. The backhaul link undergoes Nakagami-m fading channel conditions. Based on stochastic geometry theory, the closed-form expressions for the average transmission rate of UAV communication are derived. Meanwhile, the queueing delay in the buffer of the aerial Wi-Fi network is analyzed based on the queuing theory, taking into account the size of buffer, UAV flight height, and UAV deployment density. Simulation results validate our theoretical analysis, and show that using appropriate system design parameters can effectively increase the network transmission rate and reduce the queuing delay.

Wireless networks can be easily attacked by jammers due to their shared nature and open access to the wireless medium. Jamming attack can degrade the network performance significantly by emitting useless signals to the wireless channel, i.e. the access delay of nodes’ packets will increase under jamming scenarios. In order to analyze the impact of jamming attack, this paper investigates the access delay of nodes’ packets in a string multi-hop wireless network. Specially, a ring-based model is put forward to calculate the existing probability of the jammer based on the stochastic geometry theory. Then, the collision probabilities of the nodes in different locations are derived while considering the impact of neighbor nodes and jammers. At last, the access delay of the packets under IEEE 802.11 protocols is obtained. A series of numerical tests are conducted to illustrate the impact of different jamming probabilities or jammer densities on the access delay.

This article proposes a one-tier ultra-dense cellular network framework based on the characteristics of directional beamforming to explore the downlink signal-to-interference ratio (SIR) performance. The features of the proposed cellular network include: small base stations (SBSs) are equipped with directional antennas; the beam boresight directions of antennas are adaptively adjusted with the locations of users on the two-dimensional horizontal plane while the boresight directions are perfectly aligned with their associated users. In addition, an angle fading model, which follows from the exponential distribution, is developed to simulate the signals attenuation of interference links. The angle fading of the interference signals depends on the angle offset between the locations of users and the beam boresight directions of their interfering SBSs. With the assistance of stochastic geometry, the analytical expressions of coverage probability and average ergodic rate are derived by combining with the probability density function of angle offset variable. Then, the meta distribution of SIR is obtained to capture the performance changes of individual links for users. The numerical results indicate that the performance advantages of the proposed directional network obviously exceed that of the conventional isotropic antennas case. Monte-Carlo simulations validate the correctness of numerical results.