Dense Deployment Of Small Cells Research Articles

ZeroVCS: An efficient authentication protocol without trusted authority for zero-trust vehicular communication systems

Vehicular communication systems can provide two types of communications: Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V). However, in both cases, there is zero-trust between the communicating entities. This may give privilege to the unauthorized vehicles to join the network. Hence, a strong authentication protocol is required to ensure proper access control and communication security. In traditional protocols, such tasks are typically accomplished via a central Trusted Authority (TA). However, communication with TA may increase the overall authentication delay. Such delay may be incompatible with the future generation vehicular communication systems, where dense deployment of small-cells are required to ensure higher system capacity and seamless mobility (e.g., 5G onward). Further, TA may suffer from denial-of-service when the number of access requests becomes excessively large, because each request must be forwarded to TA for authentication and access control. In this article, we put forward an efficient authentication protocol without trusted authority for zero-trust vehicular communication systems, called ZeroVCS. It does not involve TA for authentication and access control, thus improving the authentication delay, reducing the chance of denial-of-service, and ensuring compatibility with the future generation vehicular communication systems. ZeroVCS can also provide communication security under various passive and active attacks. Finally, the performance-based comparison proves the efficiency of ZeroVCS.

Support vector machine‐based handover scheme for heterogeneous ultra dense network of high‐speed railway

AbstractIn order to meet the growing demands and extend network coverage for high‐speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably.

Design and performance evaluation of a 350 m free space optical communications link for pico-macrocell backhauling

Fibreless optics or free space optical communications (FSOC) has been at the forefront of many academic research in telecommunications due to its numerous benefits of large spectrum, high-speed data transmission, security, low transmit power, unlicensed spectrum and non-interfering links. Among the technical challenges of dense deployment of small cells in heterogeneous networks (HetNet) is a flexible and cost-effective backhaul link. This paper proposes, designs, simulates and evaluates the performance of a 350 m FSOC link under different atmospheric impairments for picocell to macrocell backhauling applications. The performance of the FSOC link is assessed by evaluating bit error rate (BER), eye diagram and quality factor (Q-factor). Results obtained recommend the FSOC link deployment for pico-macrocell backhauling under the weather conditions of clear sky with/without turbulence, heavy rain, heavy haze, heavy fog and wet snow.

Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning

Small cells are a promising technique to improve the capacity and throughput of future wireless networks. However, user association and power allocation in heterogeneous networks is complicated by the dense deployment of small cells, resulting in non-convex and combinatorial problems. Conventionally, machine learning techniques are applied to the joint optimization problem, which has different action spaces. Gauging the continuous spaces to discrete spaces results in the loss of granularity due to discretization (e.g. potential power values in power allocation). Due to its hybrid action space, it is sub-optimal to solve joint user association (discrete spaces) and power allocation (continuous spaces) problems by applying traditional machine learning approaches. This work proposes a Multi-Agent Parameterized Deep Reinforcement Learning (MA-PDRL) approach to address the joint user association and power allocation problem efficiently. According to simulation results, the proposed multi-agent PDRL performs better in energy efficiency and QoS satisfaction than WMMSE, game theory, Q-learning, and DRL techniques.

Network capacity improvement in 5G by using dynamic fractional frequency reuse (FFR)

The FFR approach reduces interference between macrocells and small cells/femtocells, which are installed within macrocell’s range. Dense deployment and unplanned installation of small cells and femtocells has a detrimental impact on the performance of the system as a result of the interference. Thus, the FFR concept distributes resources to maintain interference. However, some small cells/femtocells may suffer from shortage resources. In this work, dynamic FFR mechanism has been proposed. The scheme maximizes small cell/femtocell network’s capacity. The strategy aims to boost small cell/femotcell network’s capacity and system throughput. The fundamental goal of this research is to maximize the dedicated radio resources, which are allocated to small cells/femtocells, based on a predetermined cost function. According to the cost function, which is computed for all sub-regions, the proposed dynamic FFR will allocate more radio resources to small cells/femtocells, which suffer from lack resources. The highest degree of the cost function of a certain sub-region would have the largest amount of small cells/femtocells. The sub-region with the lowest cost function would receive fewer radio resources for small cell/femtocell UEs. The throughput is utilized as a metric for the purpose of evaluating the proposed strategy. MATLAB simulation is conducted for that reason. The results show that the proposed technique outperforms the fixed FFR.

Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks.

The dense deployment of small cells (SCs) in the 5G heterogeneous networks (HetNets) fulfills the demand for vast connectivity and larger data rates. Unfortunately, the power efficiency (PE) of the network is reduced because of the elevated power consumption of the densely deployed SCs and the interference that arise between them. An approach to ameliorate the PE is proposed by switching off the redundant SCs using machine learning (ML) techniques while sustaining the quality of service (QoS) for each user. In this paper, a linearly increasing inertia weight-binary particle swarm optimization (IW-BPSO) algorithm for SC on/off switching is proposed to minimize the power consumption of the network. Moreover, a soft frequency reuse (SFR) algorithm is proposed using classification trees (CTs) to alleviate the interference and elevate the system throughput. The results show that the proposed algorithms outperform the other conventional algorithms, as they reduce the power consumption of the network and the interference among the SCs, ameliorating the total throughput and the PE of the system.

On the Deployment of Small Cells in 3D HetNets With Multi-Antenna Base Stations

With the dense deployment of small cells, the impact of height difference between base stations (BSs) and user equipment (UE) on the performance of heterogeneous networks (HetNets) becomes significant. The traditional two-dimensional models are no longer sufficient to capture the three-dimensional (3D) features of dense HetNets. On the other hand, deploying multiple antennas on BSs is a promising approach to improve network capacity. In this paper, we propose a 3D model for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -tier HetNet with multi-antenna BSs, where different tiers share the same frequency band but may differ in BS height, BS density, number of antennas per BS, BS transmit power, association bias, and path loss exponent. We analytically derive the per-tier association probability under both the strongest received signal and the closest BS cell-association strategies. Based on that, we derive the expressions for the downlink ergodic rate, area spectral efficiency (ASE) and energy efficiency. The numerical results reveal that in the presence of macrocell BSs, for low to medium small-cell BS (SBS) densities, the closest BS cell-association strategy leads to low ergodic rate, ASE and energy efficiency regardless of the SBS height; while at very high SBS densities, under both cell-association strategies, SBSs should be deployed at the same height as UE to achieve high ergodic rate, ASE and energy efficiency. Moreover, we find that for a given SBS height, there exists an optimal combination of SBS density and number of antennas per SBS that maximizes the system energy efficiency.

Self-backhauling failure mitigation using 5G new radio

5G network operators will consider the dense deployment of small cells to increase network coverage and capacity. However, in order to install a large number of gNBs, operators will face the challenge of backhauling their traffic to the core network in a cost-effective manner. Although Integrated Access and Backhaul (IAB) using 5G new radio is a promising solution to solve the dilemma of small cells’ backhauling, densifying the network will increase the probability of failure of these links. To cope with this problem, a self-healing scheme is used to mitigate or at least alleviate the effect of backhaul failure. We propose to use IAB with the collaboration of neighboring gNBs to mitigate the failed backhaul link(s). Hence, our goal is to design a pre-planned network capable of providing the minimum rate requirements to its users in the presence of backhaul failure. We formulate a joint resource allocation/backhaul outage compensation optimization problem as a non-convex mixed integer non-linear program. Due to the difficulty of this problem, we divide this problem into two sub-problems, in addition to the use of different approximation and relaxation techniques to find an approximated sub-optimal solution. Simulation results show that the proposed scheme is capable of providing the network users an acceptable continuous, albeit slightly degraded, service during backhaul failure. The degradation percentage is inversely proportional to the network densification for single and multiple failures. Meanwhile, the Degree of Recovery (DoR), i.e., subtracting the degradation percentage from 100, of single failure scenarios range from 95% (for the best case) to 54% (for the worst case). For the multiple failures scenario, the DoR is much lower and can reach zero in case of three concurrent failures

A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization

AbstractThe dense deployment of small cells is a key feature of the next‐generation cellular networks aimed at providing the necessary capacity increase. But in such networks the problem of green networking will be of great importance, because the uncontrolled installation of too many cells may increase operational costs and emit more carbon dioxide. Nowadays, unmanned aerial vehicles (UAVs) offer an efficient approach to improve the quality, data rate, and meet the demanding performance requirements of applications in ultra‐dense cellular networks. This article proposes a dynamic optimization model which minimizes the overall energy consumption of UAV cellular networks in addition to guarantying the essential coverage and capacity. The proposed model optimizes user association and power utility to meet users' QoS requirements with the highest level of energy efficiency (EE). For this purpose, the surplus energy pattern of UAVs is first mathematically formulated and then applied to calculate its ruin probability. In fact, the ruin probability denotes the vulnerability of a UAV when it runs out of energy. The ruin probability is used in the next step to efficiently connect every user to the UAVs. Also, power allocation is implemented for the applications to achieve the optimal value of the accessible network's data rate through the water‐filling method. This model also performs coded routing in the multi‐hop backhauls to efficiently use the existing infrastructure of the cooperative networks for coding dual‐hop transmissions. The simulation results exhibit considerable network throughput and EE increase by 42% and 30%, respectively, for the proposed ruin‐based energy‐efficient approach in comparison with the conventional signal‐to‐interference/noise ratio‐based schemes.

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

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

Efficient Spectrum Spatial Reuse Approach Based on Gibbs Sampling for Ultra Dense Networks

The ultra dense deployment of small cells is considered a key technology to achieve the requested capacity in future cellular networks. However, the interference pattern becomes more unpredictable and challenging in these networks. Therefore, a suitable trade-off between spectrum spatial reuse and interference level has to be pursued to achieve good performance in terms of provided throughput. This paper proposes a new method to maximize the achievable throughput of an UDN with a suitable level of spatial spectrum reuse. In particular, the focus is on the small cells tier where the available spectrum is divided into sub-bands and each cell can use some of these to communicate with its associated users. The goal is to find the sub-bands allocation among cells that maximizes the system throughput. However, to limit complexity and signaling overhead that could result unaffordable in an UDN, a new metric to approximate the cell throughput to be optimized is defined. Moreover, the newly defined problem is solved using the Gibbs Sampling approach. The method effectiveness is proven by comparing the achieved results with those of the maximization of the effective system throughput and the optimal solution.

A Distributed Resource Allocation Scheme for Self-Backhauled Full-Duplex Small Cell Networks

Full-duplex (FD) radio with wireless backhauling, is a key technology for dense small cell deployments in fifth generation (5G) cellular networks. In this paper, the problem of joint user scheduling, operation mode selection and power allocation in a two-tier cellular network with FD-capable small basestations is considered. The objective is to maximize the network sum rate under quality-of-service requirements and backhaul capacity constraints. In order to solve the problem, a distributed algorithm is developed which divides the original problem into two subproblems, viz., (i) user scheduling and mode selection; and (ii) power allocation. For the first subproblem, a low-complexity search method is proposed for opportunistically finding the best user scheduling and operation modes. In the second subproblem, an iterative successive convex approximation method is proposed to transform the nonconvex power allocation problem into a sequence of convex subproblems. The proposed solution provides a hybrid strategy which opportunistically selects FD or half-duplex (HD) operation modes for access and backhaul links. It is observed that the proposed algorithm converges in few iterations and mitigates the interferences by properly adjusting the transmission powers. Numerical results demonstrate the superiority of the proposed scheme over other possible scenarios. In particular, the proposed algorithm can achieve up to 44% gain in overall sum rate compared to a network that solely uses HD.

Autonomous Mobility Management for 5G Ultra-Dense HetNets via Reinforcement Learning With Tile Coding Function Approximation

Mobility management is an important feature in modern wireless networks that can provide seamless and ubiquitous connectivity to mobile users. Due to the dense deployment of small cells and heterogeneous network topologies, the traditional handover control method can lead to various mobility-related problems, such as frequent handovers and handover failures. On the other hand, the mobility management&#x2019;s maintenance and operation cost is also increased due to increasing node density. In this paper, an autonomous mobility management control approach is proposed to increase the mobility robustness of user equipment (UE) mobility and minimize the operational cost of mobility management. The proposed method is based on reinforcement learning, which can autonomously learn an optimal handover control policy by interacting with the environment. The function approximation approach is adopted to allow reinforcement learning to process a large state and action space. A linear function approximator is used to approximate the state-action value function. Finally, the semi-gradient state-action-reward-state-action (Sarsa) method is implemented to update the approximated state-action function and learn the optimal handover control policy. The simulation results show that the proposed method can effectively improve the mobility robustness of UE under different speed ranges. Compared with the conventional reference signal received power (RSRP) based approach, the proposed approach can reduce unnecessary handovers by about 20&#x0025; and latency by 58&#x0025;, while achieving near zero handover failure rate, and increasing throughput by 12&#x0025;.

Crowdsensing-Assisted Path Loss Estimation and Management of Dynamic Coverage in 3D Wireless Networks With Dense Small Cells

Emerging vertical applications enabled by connected devices and smart infrastructures have created an ever-increasing demand for high data rates over 5th-Generation (5G) and beyond wireless networks. Deployment of dense small cells (SCs) and millimeter wave (mmWave) communication systems have become inevitable in future wireless networks. Consequently, it is more accurate to model such networks in the 3D space due to the spatially distributed nature of the SCs, locations of the devices, radio resources and propagation environment. Accurate estimation of location-specific path loss parameters is then essential for efficient utilization of radio resources and management of dynamic coverage in 3D SC networks. In the paper, a framework for location-specific path loss estimation is developed for efficient radio resource management, based on the principle of crowdsensing together with Linear Algebra (LA) and machine learning (ML) techniques considering 2.5 GHz and 28 GHz bands. The corresponding procedure for capturing dynamic coverage of a SC base station (BS) serving to an arbitrary cluster is proposed and examined based on its 3D propagation characteristics. Results show that the accuracy of 3D channel parameter estimation using gradient descent ML techniques is superior compared to LA technique and can achieve over 98% estimation accuracy. It is shown that using the proposed process, parameters can be extrapolated for the slightly extended 3D communication distances from the cluster boundary for the worst-case locations of devices based on already estimated propagation parameters with accuracy over 74% for certain distances. Although numerical results are presented for a single amorphous 3D cell of a wireless network, the framework given in the paper can be extended to any arbitrary 3D wireless cellular network.

An Energy Efficient Sleep and Wake-up Scheduling Approach in Heterogeneous Networks

In current years, there is an sudden increase of wireless data Traffic which has resulted in a huge scale dense deployment of small cells, with which the increasing cost of energy has concerned a set of research interest. I present the wireless network which consists of distributed sensor nodes. The function of distributed sensor nodes is to manage the physical condition and to generate the traffic system. Each and every nodes in WSN will sends and receives the packet ensuing in depletion of bandwidth. However, bandwidth sensor nodes are not an productive by saving the energy. Thus, sleep/wake-up scheduling is one of the vital problems in the wireless sensor network. Because, they are fixed and it cannot be recharge again. An energy efficient sleep/wake-up scheduling approach is proposed. The aim of this approach is to save the energy of each nodes and to increase their lifetimes.

LTE-U WiFi HetNets: Enabling Spectrum Sharing for 5G/Beyond 5G Systems

Traffic growth is anticipated to be 1000 times in future fifth generation (5G) networks, which necessitates dense deployment of small cells in a heterogeneous environment. Currently, heterogeneous networks (HetNets) are being considered as the most promising solution to improve coverage and capacity in both outdoor and indoor environments. However, to reap the benefits of HetNets, efficient spectrum sharing techniques are inevitable due to the scarcity of spectral resources. Traditionally, WiFi (2.4/5.0 GHz unlicensed spectrum) has been used to offload macrocells employing licensed bands in cellular networks. However, with the advent of Long Term Evolution in the unlicensed spectrum (LTE-U), offloading cellular networks has been more efficient. In this article, we describe LTE-U WiFi HetNet architecture along with deployment scenarios in detail. We outline the technical challenges that hinder the effective utilization of unlicensed bands in LTE-U WiFi HetNets. The primary challenge is to design an efficient spectrum sharing mechanism for the coexistence of different radio access technologies (i.e., LTE-U and WiFi). Continuous interference from LTE-U to WiFi results in starved WiFi users. We discuss potential solutions to this problem, and present a case study for a joint user association and power allocation method for LTE-U WiFi HetNets with the objective to maximize the sum rate.

RAPTAP: a socio-inspired approach to resource allocation and interference management in dense small cells

Femto Cells offer higher data rates to users within closed spaces. Dense deployment of small cells is a characteristic of pre-5G/LTE-Advanced Pro (LTE-A Pro) networks and is a precursor to the future 5G cellular networks. Combined with a Frequency Reuse Factor (FRF) of one, the dense small cell systems result in high co-tier interference which is undesirable. High interference Optimized scheduling decisions and interference management techniques are required to guarantee certain minimum data rates to the users at the cell-edge and increase the overall throughput of the system. This work presents a centralized scheduling approach that mitigates the detrimental impact of interference, thereby maximizing the overall throughput of the system. In doing so, we make use of concepts from Social theory and incorporate these ideas in the solution design. The centralized approach uses optimized scheduling algorithm (RAPTA) which takes feedback from the indoor users of all Femtos as the input and formulates a Mixed Integer Non-Linear Programming (MINLP) problem. Thereafter, the MINLP problem is relaxed and its solution carries out the Resource Block allocation and ensures optimal power transmission for every allocated resource block in all Femtos. We implement the proposed OPT algorithm on top of Proportional Fair (PF) in the Vienna Simulator. Since the RAPTA is NP-Hard and takes a considerably long time to solve the MINLP problem, we derive from it a polynomial time Heuristic algorithm (RAPTAP) which performs Resource Block allocation and sub-optimal Power transmission quite close to the RAPTA algorithm in terms of performance. RAPTAP is a two-stage approach each of which is inspired by ideas from two different strands of Sociological theory. We demonstrate through experiments that the proposed RAPTA + PF achieves 60.67% improvement in the services offered to the mobile users when compared to the classic PF algorithm with Soft Fractional Frequency Reuse (SFFR) interference management technique in the built environment. The Socio-inspired RAPTAP performs almost as well as the RAPTA + PF algorithm, with only a marginal 4% drop in the overall system throughput as compared to the latter. Further, we evaluate the RAPTAP heuristic in scenarios involving mobile users and demonstrate 14.52% improvement when compared to classic PF algorithm with Fractional Frequency Reuse-Full Isolation (FFR-FI) interference management. Finally, we compare the proposed Socio-inspired RAPTAP + PF with two state-of-the-art algorithms, viz., a Genetic Algorithm approach to resource allocation (NSGA-UDN) and a recent work on LTE-Unlicensed (LTE-U) + PF. Proposed Socio-inspired solution outperforms the LTE-U + PF and NSGA-UDN by 28.26% and 31.41%, respectively, in terms of throughput.

Coordinated Framework for Spectrum Allocation and User Association in 5G HetNets With mmWave

Dense deployment of small cells operating on different frequency bands based on multiple technologies provides a fundamental way to face the imminent thousand-fold traffic augmentation. This heterogeneous network (HetNet) architecture enables efficient traffic offloading among different tiers and technologies. However, research on multi-tier HetNets where various tiers share the same microwave spectrum has been well-addressed over the past years. Therefore, our work is targeted towards novel multi-tier HetNets with disparate spectrum (microwave and millimeter wave). In fact, despite the huge capacity brought by millimeter-wave technology, the latter will fail to provide universal coverage, especially indoor, and so mmWave will inevitably co-exist with a traditional sub-6GHz cellular network. In this work, we propose a coordinated user association and spectrum allocation by resorting to non-cooperative game theory. In fact, in such an arduous context, efficient distributed solutions are imperative. Extensive simulation results show the precedence of our coordinated approach in comparison with state-of-the-art heuristics. Moreover, we evaluate the impact of various network parameters, such as mmWave density, cell load, and user distribution and density, offering valuable guidelines into practical 5G HetNet design. Finally, we assess the benefit brought by massive MIMO for mmWave in such a highly heterogeneous setting.

Technologies Assisting the Paradigm Shift from 4G to 5G

Due to exponential increase in the capacity demands raised by the smart devices and multimedia applications of new generations, existing cellular networks are facing significant burden. To provide a solution to the challenges faced by 4th generation (4G) networks, there is utmost need of improving existing technologies as well as developing new technologies to meet the key requirements of 5th generation (5G) networks as well as the Next Generation Mobile Networks (NGMNs). Networks having high capacity, low latency, faster data rates and better quality of service is the vision of 5G mobile networks. In order to achieve this, we will need wider bandwidths as offered by millimeter wave bands, more spatial diversity as offered by Massive multiple input multiple output technology, denser networks as designed in dense small cell deployment technology, new waveforms using efficient coding techniques such as filtered orthogonal frequency division multiplexing and filter bank multiple carrier, a new architecture supporting virtualization and advance computing techniques as in network function virtualization, software defined networks, cloud radio access networks. Many more technologies are yet to be introduced. This tutorial gives an insight to the candidate technologies proposed by researchers till date and the progress made, for defining the standards to meet the requirements of 5G and NGMNs. It explains the basic concept of the technology and the latest development in the industry to support this technology.

Reinforcement Learning-Based Downlink Interference Control for Ultra-Dense Small Cells

The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.