Ultra-dense Heterogeneous Networks Research Articles

Robust Physical Layer Security for Power Domain Non-Orthogonal Multiple Access-Based HetNets and HUDNs: SIC Avoidance at Eavesdroppers

In this paper, we investigate the physical layer security in downlink of Power Domain Non-Orthogonal Multiple Access (PD-NOMA)-based heterogeneous cellular network (HetNet). In this paper, we assume two categories of users are available: 1) Trusted users and 2) untrusted users (eavesdroppers) at which transparency of users is not clear for the BSs, i.e., they are potential eavesdroppers. Our aim is to maximize the sum secrecy rate of the network. To this end, we formulate joint subcarrier and power allocation optimization problems to increase sum secrecy rate. Moreover, we propose a novel scheme at which the eavesdroppers are prevented from doing Successive Interference Cancellation (SIC), while legitimate users are able to do it. In practical systems, perfectly availability of all eavesdroppers' Channel State Information (CSI) at legitimate transmitters are impractical. Also CSIs of legitimate users may be also imperfect due to the error of channel estimation. Hence, we study two cases of CSI availability: 1) Perfect CSI of nodes (legitimate users and eavesdroppers) are available at the BSs and 2) imperfect CSI of nodes are available at the BSs. Since the proposed optimization problems are non-convex, we adopt the well-known iterative algorithm called Alternative Search Method (ASM). In this algorithm, the optimization problems are converted to two subproblems, power allocation and subcarrier allocation. We solve the power allocation problem by the Successive Convex Approximation approach and solve the subcarrier allocation subproblem, by exploiting the Mesh Adaptive Direct Search algorithm (MADS). Moreover, in order to study the optimality gap of the proposed solution method, we apply the monotonic optimization method. Moreover, we evaluate the proposed scheme for secure massive connectivity in Heterogeneous Ultra Dense Networks (HUDNs). Furthermore, we investigate multiple antennas base stations scenario in this literature. Finally, we numerically compare the proposed scheme with the conventional case at which the eavesdroppers are able to apply SIC. Numerical results highlight that the proposed scheme significantly improves the sum secrecy rate compared with the conventional case.

Tractable Coverage Analysis for Hexagonal Macrocell-Based Heterogeneous UDNs With Adaptive Interference-Aware CoMP

We consider a heterogeneous ultra dense network (HUDN) with both the hexagon and Poisson point process (PPP) layouts, which is more relevant for practical scenarios. A user-centric and adaptive interference-aware non-coherent coordinated multi-point transmission (IA-CoMP) scheme is used as a system setup to reduce both the cross-tier and the co-tier inter-cell interference (ICI) for the HUDN with range expansion (RE) in small cells. Due to the involvement of hexagonal macrocells, it is intractable to analyze the coverage performance of HUDNs with IA-CoMP. To this end, we present a mobile station GrouPing (MSGP)-based coverage analysis method, which partitions all MSs into four groups according to their main interference, and the whole coverage is obtained as the sum of the coverage of each MS group. We demonstrate that the proposed MSGP-based coverage analysis method can provide a tight upper bound when compared with Monte Carlo simulations. As the small cell density increases, the system coverage of HUDNs with hexagonal macrocells reduces exponentially, while the system coverage of HUDNs with PPP-based macrocells remains unchanged. Moreover, the system coverage increases with the larger one of the main ICI judging coefficient and the RE bias.

Cooperative Bargaining Based Hierarchical Spectrum Allocation Scheme for 5G Heterogeneous Networks

Fifth-generation (5G) wireless networks are proposed to meet the increasing application traffic data, which will expand rapidly. To meet this increasing data challenge, the heterogeneous ultra-dense network (HetUDN) is one of the key technologies. In this paper, we focus on the spectrum allocation problem in the HetUDN system. To enhance both spectrum efficiency and quality-of-service (QoS) provisioning, we present a novel HetUDN spectrum allocation scheme. By using right-egalitarian, extended right-egalitarian, and sequential Raiffa bargaining solutions, the proposed scheme can adaptively allocate the limited spectrum resource. To reduce the computation complexity, these bargaining solutions are hierarchically applied. Based on the characteristics of each solution, our approach can dynamically select the most adaptable allocation policy and takes various benefits in a rational way while accommodating the cascade interactions. Through simulation results, we can validate the effectiveness and efficiency of the proposed scheme. Finally, we confirm that system throughput, fairness, and service blocking probability can be improved with the proposed approach compared with the existing state-of-the-art protocols.

Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework

Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.

Delay-Oriented Cross-Tier Handover Optimization in Ultra-Dense Heterogeneous Networks

To meet the ever-growing demand and service requirement, especially for delay-sensitive services like vehicular communications, small cells consisting of low-power base stations (BSs) are deployed to undertake the traffic pressure of BSs in macro cells. The vehicle may handover from the macro cell tier to the small cell tier when moving toward a small cell. In this paper, we mainly analyze and optimize the cross-tier handover performance in heterogeneous networks from the perspective of delay characteristics. Based on the stochastic geometry theory, we deduce the expression of the effective capacity which reflects the delay to the rate before and after the handover; moreover, a resource reservation scheme is proposed. Based on this scheme, a closed-form expression of the blocking probability for delay-sensitive users is derived. Finally, based on the convex optimization theory, a joint optimization of effective capacity and blocking probability is carried out. The simulations show that the proposed optimization algorithm will greatly reduce the blocking probability while keeping the effective capacity relatively constant, especially beneficial for the delay-sensitive users. The theoretical and simulation results can be applied to the handover strategy and resource reservation strategy selection in heterogeneous networks.

CPPHA: Capability-based Privacy-Protection Handover Authentication Mechanism for SDN-based 5G HetNets

Ultra-dense Heterogeneous network (HetNet) technique can significantly improve wireless link quality, spectrum efficiency and system capacity, and satisfy different requirements for coverage in hotspots, which has been viewed as one of the key technologies in fifth Generation (5G) network. Due to the existence of many different types of base stations (BSs) and the complexity of the network topology in the 5G HetNets, there are a lot of new challenges in security and mobility management aspects for this multi-tier 5G architecture including insecure access points and potential frequent handovers among several different types of base stations. In this paper, we integrate user capability and Software Defined Network (SDN) technique, and propose a capability-based privacy protection handover authentication mechanism in SDN-based 5G HetNets. Our proposed scheme can achieve the mutual authentication and key agreement between User Equipments (UEs) and BSs in 5G HetNets at the same time largely reduce the authentication handover cost. We demonstrate that our proposed scheme indeed can provide robust security protection by employing several security analysis methods including the BAN logic and the formal verification tool Scyther. In addition, the performance evaluation results show that our scheme outperforms other existing schemes.

Joint Optimization of Area Spectral Efficiency and Energy Efficiency for Two-Tier Heterogeneous Ultra-Dense Networks

Being a promising candidate technology for the fifth-generation cellular systems, the ultra-dense network (UDN) is capable of improving the system throughput by deploying more small cells (SCs) in the existing macro-cells (MCs). In most cases, base stations (BSs) from both the MCs tier and SCs tier are modeled as two independent homogeneous Poisson point processes (PPPs) or other point processes derived from a PPP. In the previous works, the area spectral efficiency (ASE) and energy efficiency (EE) are evaluated from a typical user's perspective, and this method works well in single-user scenarios. However, there are multiple users sharing spectral resources in practical cellular systems, and the above-mentioned method cannot work in these scenarios. Therefore, we investigate the ASE and EE from the perspective of a typical BS. Analytical results show that the ASE increases with the increasing SCs density, which means that deploying more SCs in the MCs coverage area is a feasible way to enhance the system throughput. However, the energy consumption of the entire network is increasing as the SCs density increases. Therefore, we employ the firefly algorithm to jointly optimize the ASE and EE for the two-tier heterogeneous UDN. Finally, the optimal system parameters can be found under two fixed weights for the ASE and EE, respectively.

2-to-<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math> </inline-formula> Coordinated Multipoint-Based Uplink Transmission in Ultra-Dense Cellular Networks

The ongoing densification of small cells, i.e., ultimately evolving toward ultra-dense networks (UDNs), yields an unprecedented paradigm shift in network design by bringing the base stations (BSs) and users to approximate the spatial scale and number magnitude. To address the super-frequent handovers of mobile users in such UDNs, the coordinated multipoint (CoMP) then becomes a feasible solution by effectively improving communication reliability. In this paper, we consider CoMP-based uplink transmission in heterogeneous UDNs. As our first step, a CoMP uplink transmission scheme is proposed for each user to transmit to multiple (2 to $M$ ) cooperating BSs with channel inversion power control in a multi-channel scenario. The performance of the proposed uplink CoMP is analyzed in the context of UDNs using stochastic geometry, in terms of outage probability and ergodic capacity, considering the effects of BS intensity, power control, number of cooperating nodes, and network tiers, first in a single-tier network, and later extended to a $K$ -tier heterogeneous network. Performance evaluation through extensive simulations is conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve outage probability and ergodic capacity of mobile users.

Comments on “Cross-Tier Cooperation for Optimal Resource Utilization in Ultra-Dense Heterogeneous Networks”

Two adaptive dedicated channel allocation algorithms, namely dynamic dedicated channel partitioning ( ${D^2}CP$ ) and dynamic dedicated channel partitioning with cooperation ( ${D^2}CP{-}C$ ), were proposed in [1] to improve the system throughput of ultra-dense networks (UDN). However, due to the incorrect use of the geometric–arithmetic mean inequality theorem, the average system throughput could not be guaranteed to be optimal. In this letter, we study the proposed ${D^2}CP$ and ${D^2}CP{-}C$ algorithms in UDN and deduce the average system throughput. Consequently, we prove that the equal resource allocation strategy proposed in [1] is strictly not optimal.

Concurrent Optimization of Coverage, Capacity, and Load Balance in HetNets Through Soft and Hard Cell Association Parameters

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution.

Traffic Offloading With Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity.

Robust Beamforming With Pilot Reuse Scheduling in a Heterogeneous Cloud Radio Access Network

This paper considers a downlink ultradense heterogeneous cloud radio access network, which guarantees seamless coverage and can provide high date rates. In order to reduce channel state information (CSI) feedback overhead, incomplete intercluster CSI is considered, i.e., each remote radio head or macro base station only measures the CSI from user equipments (UEs) in its serving cluster. To reduce pilot consumption, pilot reuse among UEs is assumed, resulting in imperfect intracluster CSI. A two-stage optimization problem is then formulated. In the first stage, a pilot scheduling algorithm is proposed to minimize the sum mean square error (MSE) of all channel estimates. Specifically, the minimum number of required pilots along with a feasible pilot allocation solution are first determined by applying the Dsatur algorithm, and adjustments based on the defined level of pilot contamination are then carried out for further improvement. Based on the pilot allocation result obtained in the first stage, the second stage aims at maximizing the sum spectral efficiency (SE) of the network by optimizing the beam vectors. Due to incomplete intercluster CSI and imperfect intracluster CSI, an explicit expression of each UE's achievable rate is unavailable. Hence, a lower bound on the achievable rate is derived based on Jensen's inequality, and an alternative robust transmission design algorithm along with its distributed realization are then proposed to maximize the derived tight lower bound. Simulation results show that compared with the existing algorithms, the system performance can be greatly improved by the proposed algorithms in terms of both sum MSE and sum SE.

Next generation wireless cellular networks: ultra-dense multi-tier and multi-cell cooperation perspective

The next generation wireless cellular network is aimed to address the demands of users and emerging use cases set by industries and academia for beyond 2020. Hence, The next generation 5G networks need to achieve very high data rates, ultra-high reliability, extremely low latency, energy efficiency and fully connected coverage. To meet these demands, ultra-dense networks (UDN) or ultra-dense heterogeneous networks (UDHetNet), millimeter wave (mmWave) and multicell cooperation such as coordinated multipoint (CoMP) are the three leading technology enablers. In this paper, we have made an extensive survey of the current literature on 5G wireless communication focusing on UDN, mmWave and CoMP cooperation. We first discuss the architecture and key technology enablers to achieve the goals of the 5G system. Subsequently, we make an in-depth survey of underlying novel ultra-dense heterogeneous networks, mmWave and multicell cooperation. Moreover, we summarize and compare some of the current achievements and research findings for UDHetNet, mmWave and CoMP. Finally, we discuss the major research challenges and open issues in this active area of research.

DISCO: Interference-Aware Distributed Cooperation with Incentive Mechanism for 5G Heterogeneous Ultra-Dense Networks

Interference and traffic imbalance hinder improved system performance in heterogeneous ultra-dense networks. Network cooperation has become a promising paradigm with sophisticated techniques that can significantly enhance performance. In this article, a coalition game-theoretic framework is introduced to characterize cooperative behaviors, thus exploring these cooperative benefits and diversity gains. First, we introduce the basis of the coalition games. Then we survey its latest applications, in particular, interference mitigation and traffic offloading. Different from most current applications, we concentrate on cooperative incentive mechanism design since node cooperation always means resource consumption and other costs. Moreover, for the incentive mechanism, cooperative spectrum leasing is introduced. To mitigate interference and balance traffic, we propose two schemes under the presented framework: IASL and TOSL. Simulation results show the improved performance of the cooperative gains using the proposed IASL and TOSL schemes.

A New Look at Physical Layer Security, Caching, and Wireless Energy Harvesting for Heterogeneous Ultra-Dense Networks

Heterogeneous ultra-dense networks enable ultra-high data rates and ultra-low latency through the use of dense sub-6 GHz and millimeter wave (mmWave) small cells with different antenna configurations. Existing work has widely studied spectral and energy efficiency in such networks and shown that high spectral and energy efficiency can be achieved. This article investigates the benefits of heterogeneous ultra-dense network architecture from the perspectives of three promising technologies, i.e., physical layer security, caching, and wireless energy harvesting, and provides enthusiastic outlook towards application of these technologies in heterogeneous ultra-dense networks. Based on the rationale of each technology, opportunities and challenges are identified to advance the research in this emerging network.

User Association and Resource Allocation in Unified NOMA Enabled Heterogeneous Ultra Dense Networks

Heterogeneous ultra dense networks (HUDNs) and non-orthogonal multiple access (NOMA) have been identified as two proposing techniques for the fifth generation (5G) mobile communication systems due to their great capabilities to enhance spectrum efficiency. This article investigates the application of NOMA techniques in HUDNs to support massive connectivity in 5G systems. Particularly, a unified NOMA framework is proposed, including power-domain NOMA and code-domain NOMA, which can be configured flexibly to serve different applications scenarios. As a further advance, the unified NOMA framework enabled HUDNs is further investigated, with particular focuses on the user association and resource allocation. Two case studies are provided for demonstrating the effectiveness of the unified NOMA enabled HUDNs. Finally, some main challenges and promising research directions in NOMA enabled HUDNs are identified. Index Terms Heterogeneous ultra dense networks, non-orthogonal multiple access, massive connectivity, user association, and resource allocation

Heterogeneous Ultra Dense Networks: Part 2

The eleven articles in this special section focus on heterogeneous ultra dense networks. With the continuous enrichment of mobile communication application scenarios in the future, the traditional macrocellular-based mobile communication network architecture will be challenged to meet the explosive growth in demand for communications services. As described in the Guest Editorial of the first part of this Feature Topic, the ultra-dense network (UDN) has become a promising technology to signifi cantly improve the network spectral efficiency and system performance. Because the massive number of small base stations have the advantages of low transmission power and flexible deployment, they help to effectively expand the coverage and throughput of the network. Hence, in heterogeneous UDN, the dense deployment of small base stations can signifi cantly improve the quality of the links and increase the network capacity. As the key technology of 5G and the advanced communication technology at the present stage, UDN has received worldwide attention and eff orts.

On the Energy-Efficient Deployment for Ultra-Dense Heterogeneous Networks With NLoS and LoS Transmissions

We investigate network performance of ultra-dense heterogeneous networks and study the maximum energy-efficient base station deployment incorporating probabilistic non-line-ofsight and line-of-sight transmissions. First, we develop an analytical framework with the maximum instantaneous received power and the maximum average received power association schemes to model the coverage probability and related performance metrics, e.g., the potential throughput and the energy efficiency (EE). Second, we formulate two optimization problems to achieve the maximum energy-efficient deployment solution with specific service criteria. Simulation results show that there are tradeoffs among the coverage probability, the total power consumption, and the EE. To be specific, the maximum coverage probability with ideal power consumption is superior to that with practical power consumption when the total power constraint is small and inferior to that with practical power consumption when the total power constraint becomes large. Moreover, the maximum EE is a decreasing function with respect to the coverage probability constraint.

Ultra-Dense HetNets Meet Big Data: Green Frameworks, Techniques, and Approaches

Ultra-dense heterogeneous networks (Ud-HetNets) have been put forward to improve the network capacity for next-generation wireless networks. However, counter to the 5G vision, ultra-dense deployment of networks would significantly increase energy consumption and thus decrease network energy efficiency suffering from the conventional worst-case network design philosophy. This problem becomes particularly severe when Ud-HetNets meet big data because of the traditional reactive request-transmit service mode. In view of these, this article first develops a big-data-aware artificial intelligent based framework for energy-efficient operations of Ud-HetNets. Based on the framework, we then identify four promising techniques, namely big data analysis, adaptive base station operation, proactive caching, and interference-aware resource allocation, to reduce energy cost on both large and small scales. We further develop a load-aware stochastic optimization approach to show the potential of our proposed framework and techniques in energy conservation. In a nutshell, we devote to constructing green Ud-HetNets of big data with the abilities of learning and inferring by improving the flexibility of control from worst-case to adaptive design and shifting the manner of services from reactive to proactive modes.

Energy-aware interference management for ultra-dense multi-tier HetNets: Architecture and technologies

In order to improve spectral efficiency in future ultra-dense heterogeneous networks (HetNets), small cells and macro cells should be ultra-dense when deployed and dynamically overlaid. In this condition, overlaid deployment, state transition, and load migration will cause energy consumption and complex interference. Managing interference in ultra-dense multi-tier HetNets is challenging; interference can burden the network significantly when network conditions change over time. Because of the distribution of small cells and overload conditions in these ultra-dense networks, we must take into account the interference relationship among the base stations and distribution and loading conditions. An interference management scheme based on energy-aware architecture is proposed for ultra-dense multi-tier HetNets in this article. A survey is presented on energy-aware scheduling algorithms. We aim to study energy efficiency issues using graph theory and clustering. HetNets are divided into numerous interference areas, which correspond to coverage areas of each base station. Any conflict among users’ resources depends on if the interference user is located in a base station interference area, hence the use of a reinforcement-learning algorithm to optimize ongoing interference management. Given the complexity of a multi-layer network, conflict graph theory can easily identify network dynamics because it focuses on users’ received interference. Conflict graph theory can also increase resource reusability and efficiency. The proposed scheme can allocate the frequency spectrum equitably, reduce system interference, and improve throughput performance.