Heterogeneous Cellular Networks Research Articles

Pioneering Methods for Enhancing PPI and Phenotype Networks for Candidate Disease Prioritization

The physical contacts of high-specificity between two or more protein molecules constitute Protein-Protein Interactions (PPIs). PPI networks are modeled through graphs where node denotes proteins and edges denote interaction between proteins. The PPI network plays an important role to identify the interesting disease gene candidates. But, the PPI network usually contains false interactions. Many techniques have been proposed to reconstruct PPI network to remove false interactions and improve ranking of candidate disease. Random Walk with Restart on Diffusion profile (RWRDP) and Random Walk on a Reliable Heterogeneous Network (RWRHN) was two among them. In these methods, Gene topological similarity was incorporated with original PPI network to reconstruct new PPI network. Phenotype network was constructed by calculating similarity between gene phenotypes. The reconstructed network and phenotype networks were combined to rank candidate disease genes. However, the PPI reconstruction was fully related with the quality of protein interaction data. In order to enhance the reconstruction of PPI, a Piecewise Linear Regression (PLR) based protein sequence similarity measure and Bat Algorithm based gene expression similarity were proposed with RHN. In this paper, additional measure called Interaction Level Sub cellular Localization Score (ILSLS) is proposed to further reduce the false interaction in the reconstruction of PPI network. ILSLS is the combination of Normalized Sub cellular Localization score (NSL) and Protein Multiple Location Prediction score (PMLP). The proposed work is named as Random Walker on Optimized Trustworthy Heterogeneous Sub Cellular localization aware Network (RW-OTHSN). In order to enhance the ranking of RW-OTHSN, phenotype structure is considered while construction phenotype network to rank the candidate disease genes. The phenotype structure is characterized based on h*-sequence model which identify highly discriminative signatures with only a small number of genes. This proposed work is named as Random Walker on Optimized Trustworthy Heterogeneous Sub Cellular localization and Phenotype structure aware Network (RW-OTHSPN). The efficiency of the proposed methods are evaluated on PPI network database in terms of Average degree, Relative Frequency for PPI reconstruction, Number of successful predictions, precision and recall for candidate disease gene ranking.

Outage Probability Analysis of User-Centric SBS-Based HCNets Under Hybrid Rician/Rayleigh Fading

To model dense user equipment (UE) distribution in heterogeneous cellular networks (HCNets), the Poisson cluster process (PCP) has emerged as a promising tool. In user-centric HCNets where UEs are distributed according to a PCP around a small base station (SBS), the network performance has been commonly studied in literature under a Rayleigh fading environment assumption. However, such an assumption may not hold in user-centric HCNets given the possible existence of a strong line-of-sight (LOS) link between UEs and BSs due to a relatively short transmission distance. This letter analyzes the performance of user-centric SBS based HCNets by considering that the desired LOS link experiences Rician fading whereas interferer links experience non-LOS Rayleigh fading. Numerical results, validated through extensive simulations, demonstrate that the outage probability decreases under this hybrid Rician/Rayleigh fading environment.

Coverage and Energy Efficiency Analysis for Two-Tier Heterogeneous Cellular Networks Based on Matérn Hard-Core Process

Due to the dense deployment of base stations (BSs) in heterogeneous cellular networks (HCNs), the energy efficiency (EE) of HCN has attracted the attention of academia and industry. Considering its mathematical tractability, the Poisson point process (PPP) has been employed to model HCNs and analyze their performance widely. The PPP falls short in modeling the effect of interference management techniques, which typically introduces some form of spatial mutual exclusion among BSs. In PPP, all the nodes are independent from each other. As such, PPP may not be suitable to model networks with interference management techniques, where there exists repulsion among the nodes. Considering this, we adopt the Matérn hard-core process (MHCP) instead of PPP, in which no two nodes can be closer than a repulsion radius from one another. In this paper, we study the coverage performance and EE of a two-tier HCN modelled by Matérn hard-core process (MHCP); we abbreviate this kind of two-tier HCN as MHCP-MHCP. We first derive the approximate expression of coverage probability of MHCP-MHCP by extending the approximate signal to interference ratio analysis based on the PPP (ASAPPP) method to multi-tier HCN. The concrete SIR gain of the MHCP model relative to the PPP model is derived through simulation and data fitting. On the basis of coverage analysis, we derive and formulate the EE of MHCP-MHCP network. Simulation results verify the correctness of our theoretical analysis and show the performance difference between the MHCP-MHCP and PPP modelled network.

Multitier heterogeneous network using stochastic geometry and factorial moment approaches

SummaryThe rapid growth of small cells is driving cellular network toward randomness and heterogeneity. Usually, cellular networks are modeled by placing each tier (eg, macro, pico, and relay nodes) deterministically on a grid. In such a heterogeneous cellular network, the rational approach to characterize the base stations (BSs), user, and relay locations is by using random spatial models. When calculating the metric performances such as coverage probability, these networks are idealized without consideration of interference. Therefore, interference modeling remains the key issue for the deployment of small cells. This paper developed a single and multitier cellular network model that captures the downlink heterogeneous cellular network with variable parameters such as the target signal‐to‐interference ratio (SIR), transmitted power, and deployment density. In particular, we model ‐tier transmission and compare it with a single‐tier and traditional grid model to obtain tractable coverage probability using stochastic geometry and factorial moment. The obtained results demonstrate the effectiveness and analytical tractability to study the heterogeneous performance.

An Offloading Mechanism Towards SE–EE Experiences Tradeoff in Heterogeneous Cellular Networks

To achieve a tradeoff between spectral efficiency (SE) and energy efficiency (EE) experiences, we design an offloading mechanism to maximize the sum of weighted logarithmic utilities with respect to SE and EE for heterogeneous cellular networks, where the weighting parameters are used for adjusting the SE–EE experiences. Unlike the most existing offloading mechanisms that often try to find a tradeoff between SE and EC (energy consumption), we concentrate on a tradeoff between SE–EE experiences in our mechanism. That is to say, our mechanism directly optimizes EE and is weighted in favour of SE–EE fairness. To solve the finally formulated problem in a mixed-integer and nonlinear form, we firstly make some relaxation for the optimization objective to obtain its an upper bound. Then, we perform a decoupling operation to achieve a decomposable form of dual problem. Finally, we develop a feasible algorithm that can be well implemented in a distributed manner. Numerical results show that the designed mechanism can definitely reach a tradeoff between SE–EE experiences.

Proactive Uplink Interference Mitigation in HetNets Stressed by Uniformly Distributed Wideband Jammers

In heterogeneous cellular networks (HetNets), densification of small base stations (SBSs) in the macro base station (MBS) coverage region leads to improved network coverage and capacity. However, this improvement is at the cost of inter-cell interference (ICI) due to the high MBS transmit power. The situation deteriorates further when uniformly deployed wideband jammers (WBJs) to initiate distributed denial-of-service (DDoS) attacks are present. With sufficient knowledge about the network parameters, WBJs produce substantial jammer interference (JI) by transmitting unwanted energy in the legitimate band. Such jamming attacks can cause significant JI in the UL communications of MBS edge users (M-EUs) due to i) low uplink (UL) transmit power of user equipment (UE) and ii) longer distances of M-EUs. As a result of ICI and JI, M-EUs are severely affected and go off the coverage due to a low received signal-to-interference ratio (SIR). Hence, to mitigate both ICI and JI, we use a proactive resource allocation scheme known as reverse frequency allocation (RFA). The results indicate that although ICI and JI significantly degrade network performance, RFA employment leads to improved SIR due to effective ICI and JI mitigation.

Bender's Decomposition for Optimization Design Problems in Communication Networks

Various types of communication networks are constantly emerging to improve connectivity services and facilitate the interconnection of various types of devices. This involves the development of several technologies, such as device-to-device communications, wireless sensor networks and vehicular communications. The various services provided have heterogeneous requirements on the quality metrics such as throughput, end-to-end latency and jitter. Furthermore, different network technologies have inherently heterogeneous restrictions on resources, for example, power, interference management requirements, computational capabilities, and so on. As a result, different network operations such as spectrum management, routing, power control and offloading need to be performed differently. Mathematical optimization techniques have always been at the heart of such design problems to formulate and propose computationally efficient solution algorithms. One of the existing powerful techniques of mathematical optimization is Benders Decomposition (BD), which is the focus of this article. Here, we briefly review different BD variants that have been applied in various existing network types and different design problems. These main variants are the classical, the combinatorial, the multi-stage, and the generalized BD. We discuss compelling BD applications for various network types including heterogeneous cellular networks, infrastructure wired wide area networks, smart grids, wireless sensor networks, and wireless local area networks. Mainly, our goal is to assist the readers in refining the motivation, problem formulation, and methodology of this powerful optimization technique in the context of future networks. We also discuss the BD challenges and the prospective ways these can be addressed when applied to communication networks' design problems.

Intelligent user association algorithm in heterogeneous wireless cellular networks

In heterogeneous cellular networks, user association is the key for network performance. In addition, reasonable bandwidth allocation of backhaul network can make full use of network resources and thus improve network performance. In view of this, the authors propose an intelligent user association algorithm considering backhaul bandwidth allocation to maximise the weighted sum rate of network while guaranteeing the requirements of quality of service and avoiding the congestion of backhaul network. Firstly, they predict traffic of each base station using adaptive wavelet decomposition and back-propagation neutral network, which considers the tradeoff between accuracy and complexity. The appropriate wavelet decomposition level can be adaptively selected according to the different mean and variance of traffic. Then, backhaul bandwidth is allocated to different base stations according to the predicted results. Finally, they adopt a method based on penalty function and Marks-Wright algorithm, which transforms the mixed integer programming problem into a series of convex optimisation problems that can be solved iteratively, to complete the user association. The simulation results show that the prediction algorithm obviously reduces error and the association algorithm has fast convergence speed.

Online Antenna Tuning in Heterogeneous Cellular Networks With Deep Reinforcement Learning

We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The interactions between the cells, most notably due to their coupled interference render this optimization prohibitively complex. Utilizing a single agent reinforcement learning (RL) algorithm for this optimization becomes quite suboptimum despite its scalability, whereas multi-agent RL algorithms yield better solutions at the expense of scalability. Hence, we propose a two-step compromise algorithm. Specifically, a multi-agent mean field RL algorithm is first utilized in the offline phase so as to transfer information as features for the second (online) phase single agent RL algorithm, which employs a deep neural network to learn users locations. This two-step approach is a practical solution for real deployments, which should automatically adapt to environmental changes in the network. Our results illustrate that the proposed algorithm approaches the performance of the multi-agent RL, which requires millions of trials, with hundreds of online trials, assuming relatively low environmental dynamics, and performs much better than a single agent RL. Furthermore, the proposed algorithm is compact and implementable, and empirically appears to provide a performance guarantee regardless of the amount of environmental dynamics.

Geometric Approach Based Resource Allocation in Heterogeneous Cellular Networks

Recently, with the evolutionary development of intelligent devices, the rapid increasing demand of mobile data traffic and heavy energy consumption in wireless networks become critical problems. Small cell network is considered as a promising solution to improve the communication quality of user equipments and increase the network transmission rate. Nevertheless, the increasing number of small base stations could extremely consume the extra electric energy and reduce network energy efficiency. To improve network energy efficiency, a clustering and geometric approach based power allocation algorithm is designed, which could guarantee the loading balance and the outage probability of heterogeneous cellular networks. Initially, a semi-definite programming based correlation clustering algorithm is introduced for femtocell base stations (FBSs) cluster formation to eliminate the intra-layer interference. Moreover, an energy efficiency and geometric approach based power allocation algorithm combines with the dynamic FBSs on/off strategy could further improve network energy efficiency. Thereby, the associated user equipments could be transferred to the intra-cluster FBSs by the dual connectivity method. Finally, numerical simulation results demonstrate that the proposed algorithm could achieve superior performance in terms of network energy efficiency compared with algorithms in literatures.

Millimeter Wave Integrated Access and Backhaul in 5G: Performance Analysis and Design Insights

With the emergence of integrated access and backhaul (IAB) in the fifth generation (5G) of cellular networks, backhaul is no longer just a passive capacity constraint in cellular network design. In fact, this tight integration of access and backhaul is one of the key ways in which 5G millimeter wave (mm-wave) heterogeneous cellular networks (HetNets) differ from traditional settings where the backhaul network was designed independently from the radio access network (RAN). With the goal of elucidating key design trends for this new paradigm, we develop a stochastic geometry-based analytical framework for a millimeter wave (mm-wave) two-tier HetNet with IAB where only the macro BSs (MBSs) have fiber access to the core network and the small cell BSs (SBSs) are wirelessly backhauled by the MBSs over mm-wave links. For this network, we derive the downlink rate coverage probability for two types of resource allocations at the MBS: 1) integrated resource allocation (IRA): where the total bandwidth (BW) is dynamically split between access and backhaul, and 2) orthogonal resource allocation (ORA): where a static partition is defined for the access and backhaul communications. Our analysis concretely demonstrates that offloading users from the MBSs to SBSs may not provide similar rate improvements in an IAB setting as it would in a HetNet with fiber-backhauled SBS. Our analysis also shows that it is not possible to improve the user rate in an IAB setting by simply densifying the SBSs due to the bottleneck on the rate of wireless backhaul links between MBS and SBS.

Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-Latency

In this paper, we study the coexistence and synergy between edge and central cloud computing in a heterogeneous cellular network (HetNet), which contains a multi-antenna macro base station (MBS), multiple multi-antenna small base stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are empowered by edge clouds offering limited computing services for UEs, whereas the MBS provides high-performance central cloud computing services to UEs via a restricted multiple-input multiple-output (MIMO) backhaul to their associated SBSs. With processing latency constraints at the central and the edge networks, we aim to minimize the system energy consumption used for task offloading and computation. The problem is formulated by jointly optimizing the cloud selection, the UEs' transmit powers, the SBSs' receive beamformers, and the SBSs' transmit covariance matrices, which is a mixed-integer and non-convex optimization problem. Based on the methods such as decomposition approach and successive pseudoconvex approach, a tractable solution is proposed via an iterative algorithm. The simulation results show that our proposed solution can achieve great performance gain over conventional schemes using edge or central cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO backhaul can significantly reduce the complexity of the proposed algorithm and obtain even better performance.

A Novel Content Placement Strategy for Heterogeneous Cellular Networks With Small Cells

The proliferation of multimedia services creates an explosive growth of video traffic in cellular networks, which is oftentimes caused by requests from different users of the same (popular) video content. We model the problem of optimal placement of popular video files in the cache of small cells as a 0/1 Knapsack problem, and use dynamic programming to solve it. The performance of the obtained optimal solution is evaluated and compared with that of other ad-hoc placement strategies. Useful guidelines for the design of cache-enabled cellular networks are also given.

New Efficient Transmission Technique for HetNets With Massive MIMO Wireless Backhaul

In order to cope with the rapid increase in power consumption of heterogeneous cellular networks, this article proposes a new efficient transmission technique for heterogeneous networks with massive MIMO wireless backhaul with the objective of minimizing power consumption cost. We assume that transmissions on the backhaul link and the access link occur simultaneously on the same frequency band (in-band) thanks to MIMO spatial multiplexing. On the other hand, we consider that uplink and downlink transmissions are separated in time. In order to prevent multi-user and inter-tier interference, block diagonalization beamforming is considered at the macro base station (MBS) and the signal from the small base station (SBS) to the MBS is transmitted orthogonally to the channel of the SBS's users. The problem of minimizing the transmit power of base stations under users’ minimum-rate constraints is formulated. We first derive analytically the optimal time splitting parameter and the allocated transmit power considering that the inter-SBS interference is generated by fixed power. Next, we solve the power allocation problem when the generated inter-SBS interference is no longer considered fixed by proposing an efficient iterative power allocation algorithm. A heuristic user scheduling algorithm is devised in order to deal with the feasibility problem. Finally, simulations validate our analysis and show that the proposed transmission technique outperforms the conventional reverse time division duplex with bandwidth splitting (out-band) in terms of total power consumption.

Effect of intercell interference on cell boundary users in three‐cell and seven‐cell HetCN

SummaryIndoor and cell‐edge coverage has been a major issue of concern for predeployed traditional macrocell (MC)–based homogeneous cellular network. Moreover, with the extensive increase of mobile users and developments of smart and highly specified devices, user demands and activities have led to huge cellular traffic. The key solutions to these that include network upgradation, overlaying of small cells (SCs), and scaling of resources have turned out to be the major causes for intercell interference (ICI) and energy‐efficiency degradation in heterogeneous cellular networks (HetCNs). In this paper, authors have tried to analyze the downlink performance metrics of cell boundary users with MCs overlaying SCs for three‐cell circular and seven‐cell sectorized networks through frequency reuse (FR) schemes. This paper also discusses the impact of ICI being encountered by users and the effect of SCs on the energy efficiency of the network. The locations for SCs are perceived where user density is large and demands high data rate such as at hot‐spot (HS) areas, railway stations, shopping malls, working farms, and organization. The performance metrics sum rate, average user throughput, and energy efficiency are compared by employing FR‐1 (full spectrum) and FR‐3 (three subbands) among MCs and deployed SCs. For both scenarios, simulation results and analyses depict that without SCs, utilization of FR‐1 results in performance degradation due to ICI effects. However, the downlink performance of cell boundary user and energy efficiency of the network could be enhanced by overlaying SCs near cell boundaries of preexisting MCs along with the allocation of FR‐1.

Rate and coverage analysis in multi-tier heterogeneous network using stochastic geometry approach

To meet massive wireless traffic demand in the upcoming fifth generation cellular networks, the rapid growth of small cells is emerging as an attractive solution and deriving cellular network toward randomness and heterogeneity. In a typical macrocell, there are many deployed femtocells and few picocells with unplanned users. In such a heterogeneous cellular network, using random spatial models is a rational approach to characterize the base stations (BS) locations. The key issue for the deployment of small cells is interference management. Considering multi-tier cellular network, in this paper we develop a general kth-tier BSs model that captures the downlink multi-antenna heterogeneous cellular network with variable parameters such as the number of antenna, target signal-to-noise-and-interference ratio (SINR), transmitted power, and deployment density. In particular, we consider K−tier transmission and compare it with a single-tier, traditional grid model and multi-antenna ultra-dense network (UDN) model to obtain tractable rate and coverage probability. The results showed that the proposed model outperforms the traditional multi-antenna ultra-dense network (UDN) model and its accuracy is confirmed to be similar to the traditional grid model. In addition, a comparison with the counterpart uplink model is provided, which reveals that the coverage probability of the proposed downlink model decreases when the fractional power control (ϵ) increases in the uplink.

Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks

Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the computationally expensive problem with the large action space, multi-agent deep RL method is proposed. Specifically, the state, action and reward function are defined for UEs, and dueling double deep Q-network (D3QN) strategy is introduced to obtain the nearly optimal policy. Through message passing, the distributed UEs can obtain the global state space with a small communication overhead. With the double-Q strategy and dueling architecture, D3QN can rapidly converge to a subgame perfect Nash equilibrium. Simulation results demonstrate that D3QN achieves the better performance than other RL approaches in solving large-scale learning problems.

A distance‐sensitive distributed repulsive sleeping approach for dependable coverage in heterogeneous cellular networks

AbstractBase station (BS) sleeping technology has become one of the significant technologies in fifth‐generation green communications. However, enormous communication overhead and coverage holes produced by existing sleeping strategies will decline the robustness of the network. To tackle this problem, this paper proposes a distance‐sensitive distributed repulsive sleeping strategy (DSDRSS), based on hard‐core point process (HCPP). First, through information exchanges, presleeping BSs in the same region form a sleeping cluster (SC) whose size is limited by sleeping distance. Second, BSs in the SC perform BS sleeping with a mark method where BSs will be randomly assigned a mark, and BSs with the lowest mark will remain on to ensure the coverage. Third, to characterize the performance of DSDRSS, the analytical expressions of sleeping probability, coverage probability, and average achievable rate for user equipment (UE) under DSDRSS are derived. Finally, the coverage characteristics of UE under DSDRSS are analyzed and compared with those under different sleeping operations. DSDRSS realizes sleeping operations through the cooperation between BSs in an SC, not relying on the feedback links between a small BS and the control center. As a result, DSDRSS can not only enable flexible perception of traffic changes in sleeping area but also complete sleeping with less overhead. The simulation results show that DSDRSS supports more dependable coverage compared with random sleeping strategy and general repulsive sleeping strategy.

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.

User rate and power optimization for HetNets under Poisson cluster process

Heterogeneous cellular networks (HetNets) consist of different tiers of base stations (BSs) to meet the ever-increasing mobile traffic demand. Due to the random deployment of BSs, Poisson point process (PPP) is often used to model the BS distribution. However, low power small cells are usually clustered around the popular areas, and PPP can not reflect such a feature. To this end, we in this paper consider base station (BS) cooperation and analyze user rate and energy efficiency of HetNets based on a Poisson cluster process (PCP). A calculable formula for the average data rate (or spectral efficiency) and its approximated closed form are derived. Based on this closed form, a power minimization solution with certain spectral efficiency constraint is proposed, and the optimal cooperation radii are derived. Furthermore, we analyze spectral efficiency under a limited number of cooperative BSs in a two-tier network. Finally, we propose a range expansion (RE) scheme and examine the impact of this scheme. The theoretical analyses are verified by simulation results.