Heterogeneous Small Cell Networks Research Articles

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 Resource Allocation for Uplink MC-NOMA Based Heterogeneous Small Cell Networks With Wireless Backhaul

In this paper, we investigate energy efficient resource allocation for uplink multi-carrier non orthogonal multiple access (MC-NOMA) based two-tier heterogeneous networks (HetNets) with wireless backhaul. We have exploited channel diversity by assigning multiple subchannels to various MC-NOMA users (NUs), while allowing multiple NUs to use the same subchannel. We formulate a joint NU clustering, and power and subchannel allocation problem for system's energy efficiency (EE) maximization under the constraints of minimum quality of service (QoS) requirement, maximum allowable transmit power for each NU, and successive interference cancelation. The formulated problem is non-convex with fractional objective function, and therefore, to convert it into a tractable from, it is divided into two subproblems, i.e., subchannel allocation and NU clustering subproblem, and power allocation subproblem. The subchannel allocation and NU clustering subproblem is solved through a suboptimal algorithm, which exploits the channel diversity by maintaining maximum possible difference in transmit power levels of clustered NUs. Whereas for the power allocation subproblem, the EE optimization problem is convexified through Dinkelbach and successive convex approximation techniques and a closed-form suboptimal solution is obtained. The simulation results show that the proposed energy efficient algorithm outperforms the existing NOMA and orthogonal multiple access (OMA) algorithms. The proposed approach also guarantees user fairness by giving priority to the NUs with unfulfilled throughput requirements.

Joint user association and fair scheduling for dual connectivity heterogeneous networks

Heterogeneous small cell network is one of the most effective solutions to overcome spectrum scarcity for the next generation of mobile networks. Dual connectivity (DC) can improve the throughput for each individual user by allowing concurrent access to two heterogeneous radio networks. In this paper, we propose a joint user association and fair scheduling algorithm (JUAFS) to deal with the resource allocation and load balancing issues for DC heterogeneous small cell networks. Considering different coverage sizes, numbers of users, and quality of experience characteristics of heterogeneous cells, we present a proportional fair scheduling for user association among cells and utilize interference graph to minimize the transmission conflict probability. Simulation results show the performance improvement of the proposed algorithm in spectrum efficiency and fairness comparing to the existing schemes.

Unlicensed band sharing based on bargaining game for heterogeneous networks

Currently, limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more. It is necessary to extend wireless communication networks to unlicensed spectrum. In this paper, we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency, where one radio access technology (RAT) (such as macro cellular network) "competes" the unlicensed bands with multiple other RATs (such as small cellular networks or Wi-Fi) virtually. Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom, we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks, where bargaining loss and time dissipation loss for virtual "price" of unlicensed bands are mainly considered. In the one-to-many bargaining process, we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner. Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.

Optimal ADMM-Based Spectrum and Power Allocation for Heterogeneous Small-Cell Networks with Hybrid Energy Supplies

Powering cellular networks with hybrid energy supplies is not only environment-friendly but can also reduce the on-grid energy consumption, thus being emerging as a promising solution for green networking. Intelligent management of spectrum and power can increase the network utility in cellular networks with hybrid energy supplies, usually at the cost of higher energy consumption. Unlike prior studies on either the network utility maximization or on-grid energy cost minimization, this paper studies the joint spectrum and power allocation problem that maximizes the system revenue in a heterogeneous small-cell network with hybrid energy supplies. Specifically, the system revenue is considered as the difference between the network utility and on-grid energy cost. By developing the convexity of the optimization problem through transformation and reparameterization, we propose a joint spectrum and power allocation algorithm based on the primal-dual arguments to obtain the optimal solution by iteratively solving the primal and dual sub-problems of the convex optimization problem. To solve the primal sub-problem, we further propose the Lagrangian maximization based on the alternating direction method of multipliers (ADMM), and derive the optimal solution in the closed-form expression at each iteration. It is shown that the proposed joint spectrum and power allocation algorithm approaches the global optimality at the rate of 1=n with n being the number of iterations. Also, the proposed ADMM-based Lagrangian maximization algorithm approaches the primal optimal solution with the time complexity of O(1=ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ) iterations with ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> being the termination parameter. Simulation results show that in comparison with the power control with equal frequency allocation algorithm and frequency allocation with equal power allocation algorithms the proposed algorithm increases the system revenue by over 20 and 60 percent without consuming more on-grid energy when the proportional fairness utility and the weighted sum rate utility are considered with the approximate system parameter settings, respectively. Meanwhile, in comparison with the full frequency reuse case, the proposed algorithm increases the system revenue by 20 percent at least in terms of the weighted sum rate utility, although it achieves the similar system revenue when considering the proportional fairness utility. Simulation results also show that our proposed algorithm can perform well under the realistic fast fading channel conditions.

Maximizing Fairness for Resource Allocation in Heterogeneous 5G Networks

In this article, we first formulate the joint resource allocation, interference minimization, user-level, and cell-level fairness for maximum resource reuse in 5G heterogeneous small cell networks as an NP-hard problem. We then propose three algorithms - centralized, distributed, and randomized distributed algorithms - to efficiently solve the formulated resource allocation problem while minimizing interference, maximizing fairness, and resource reuse. Through extensive real data analysis and network simulations, we show that our proposed solutions outperform state-of-the-art schemes, namely interfering model (INT) and distributed random access (DRA), for both low and high-density 5G networks.

Victim Aware AP-PF CoMP Clustering for Resource Allocation in Ultra-Dense Heterogeneous Small-Cell Networks

Heterogeneous networks with dense deployment of femto cells has provided the promising solution to enhance the system throughputs for the next generation wireless communication. When the large number of heterogeneous networks are overlapped, then traditional intercell interface technique failed to mitigate the interference in between the cells. So, to mitigate the interference, it requires advanced approach for improving the cell edge throughputs and spectral efficiency. For this, the paper presents a frame work to allocate the efficient resource among the users in dense networks. We proposed affinity propagation unsupervised learning to form the cluster with center and then regularized the cluster for effectively allocated the resource. Users on the cluster edge has suffering the inter cluster interface, a victim aware and coordination multipoint mechanism is further proposed to allocated the required resources for these victimized users. We analyzed the performance of our proposed framework with proportional fair based criteria. The total throughputs, edge throughput and spectral efficiency of the system are significantly enhanced in our simulation results through this proposed framework.

Modeling multi-tier heterogeneous small cell networks: rate and coverage performance

The rapid growth of small cells is driving cellular network toward randomness and heterogeneity. The multi-tier heterogeneous network (HetNet) addresses the massive connectivity demands of the emerging cellular networks. Cellular networks are usually modeled by placing each tier (e.g macro, pico and relay nodes) deterministically on a grid which ignores the spatial randomness of the nodes. Several works were idealized for not capturing the interference which is a major performance bottleneck. Overcoming such limitation by realistic models is much appreciated. Multi-tier relay cellular network is studied in this paper, In particular, we consider {mathscr {K}}-tier transmission modeled by factorial moment and stochastic geometry and compare it with a single-tier, traditional grid model and multi-antenna ultra-dense network (UDN) model to obtain tractable rate coverage and coverage probability. The locations of the relays, base stations, and users nodes are modeled as a Poisson Point Process. The results showed that the proposed model outperforms the traditional multi-antenna UDN model and its accuracy is confirmed to be similar to the traditional grid model. The obtained results from the proposed and comparable models demonstrate the effectiveness and analytical tractability to study the HetNet performance.

An Energy-Efficient Framework for Internet of Things Underlaying Heterogeneous Small Cell Networks

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of Things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users. Extensive simulations demonstrate that our proposed framework can significantly enhance the energy efficiency for IoT underlaying small cell networks with guaranteed outage probability.

Green resource allocation and energy management in heterogeneous small cell networks powered by hybrid energy

In heterogeneous networks (HetNets), how to improve spectrum efficiency is a crucial issue. Meanwhile increased energy consumption inspires network operators to deploy renewable energy sources as assistance to traditional electricity. Based on above aspects, we allow base stations (BSs) to share their licensed spectrum resource with each other and adjust transmission power to adapt to the renewable energy level. Considering the sharing fairness among BSs, we formulate a multi-person bargaining problem as a stochastic optimization problem. We divide the optimization problem into three parts: data rate control, resource allocation and energy management. An online dynamic control algorithm is proposed to control admission rate and resource allocation to maximize the transmission and sharing profits with the least grid energy consumption. Simulation results investigate the time-varying data control and energy management of BSs and demonstrate the effectiveness of the proposed scheme.

Improving symbol error rate of heterogeneous network by clustering, IA, and interference detection

Despite all the benefits of small cells in heterogeneous networks (HetNets) such as coverage improvement and spectral efficiency, they may cause additional interferences. Interference alignment (IA) by cell clustering is a possible candidate to manage HetNets interferences. Several picocells coexist within a microcell area while the nodes that cause interferences in a similar range of the desired signals are grouped in a cluster. IA removes the intra-cluster interferences, and the strong remaining interferences are estimated by interference detection. The simulation results show improvement in the symbol-error rate by the proposed scheme.

Vertical Handover Analysis for Randomly Deployed Small Cells in Heterogeneous Networks

In this paper, we analyze vertical handovers in heterogeneous networks. Vertical handovers are an important ramification of user mobility in a heterogeneous wireless network since they directly impact network performance, such as throughput, packet delay, or call blocking probability. However, the study of vertical handovers faces many difficulties when it comes to analysis, including network modeling which is extremely complicated due to topology diversity. To tackle such analytic challenges, we propose a stochastic geometric analysis scheme on user mobility and small cell networks, in order to capture the impacts of different topologies. We derive a theoretical expression for the expected number of handovers that occur and a corresponding approximation. Our analysis shows that the number of vertical handovers is determined as a concave function of small cell radius r when given the small cell density. Via simulations our analysis is verified in various wireless environments.

Caching for Mobile Social Networks with Deep Learning: Twitter Analysis for 2016 U.S. Election

As the rise of the portable devices, people usually access the social media such as Twitter and Facebook through wireless networks. Therefore, data transmission rates significant important to the end users. In this work, we discuss the problem of context-aware data caching in the heterogeneous small cell networks to reduce the service delay and how the device-to-device (D2D) and device-to-infrastructure (D2I) improve the system social welfare. In the data-caching model, we explore three types of cache entities, macro cell base stations, small cell base stations, and end user devices. We propose a long short-term memory (LSTM) deep learning model to perform data analysis and extract information content from the data. By knowing the interest of the data to the cache entities, we can cache the data that will most likely to be requested by the end users to reduce service latency. In simulation, we show our proposed algorithm can efficiently reduce the service latency during 2016 U.S. presidential election where mobile user were urgent to request the election information through wireless networks. Comparing with other mechanisms such as using one-to-many matching algorithm or without D2D communication technology, our proposed algorithm improves significantly on the devices performance and system social welfare.

Energy Efficient Resource Management in SWIPT Enabled Heterogeneous Networks With NOMA

Non-orthogonal multiple access (NOMA) in heterogeneous network (HetNet) is a very promising scheme to meet the exponential growth of mobile data expected in the coming years. However, since wireless networks are becoming denser, the energy consumption of such networks is increasingly severe. Therefore, it is necessary to design novel energy efficiency (EE) maximization technologies under the constraint of limited energy supply. This paper investigates the resource optimization problem of NOMA heterogeneous small cell networks with simultaneous wireless information and power transfer (SWIPT). By decoupling subchannel allocation and power control, a low-complexity subchannel matching algorithm is designed. Furthermore, to maximize the energy efficiency, a power optimization algorithm is proposed using Langrangian duality. Aiming at the power allocation problem, the original non-convex and non-linear energy efficiency optimization problem is transformed into a more tractable one. Simulation results demonstrate the effectiveness and convergence of the proposed optimization scheme in terms of system energy efficiency.

Regret Matching Learning Based Spectrum Reuse in Small Cell Networks

We investigate the interference-aware spectrum reuse for heterogeneous small cell networks (SCNs), by specially considering dense-user deployment and stochastic-environment uncertainties. Most existing approaches, which are lack of evolving coordinations and rely on precise channel state information, tend to be inefficient in the context of dense SCNs with uncertainties. To improve the performance, by introducing a reliable metric of successful transmission probability to characterize the individual utility, we adopt a correlated equilibrium (CE)-based game to formulate spectrum reuse, and propose a distributed regret-matching learning algorithm to achieve the CE solutions. Eliminating the dependence on definite information and with general CE points consideration, our new scheme is feasible under the varying environment and can obtain more promising solutions than the state-of-art reinforcement learning methods by encouraging players to coordinate their strategies. Numerical simulations demonstrate the advantages of our proposed scheme.

Interference-Aware Dynamic Channel Allocation for Small-Cells in Heterogeneous Networks with FFR

Interference-Aware Dynamic Channel Allocation for Small-Cells in Heterogeneous Networks with FFR

Game theoretic handover optimisation for dense small cells heterogeneous networks

In this study, the authors formulate a non-cooperative game approach in which all base stations compete in a selfish manner to transmit at higher power. Each base station in the network is considered as a player in the game. The solution of the game is obtained by finding the optimal point, namely the Nash equilibrium. The proposed method, named efficient handover game theoretic, targets to manage the handover in dense small cell heterogeneous networks. Each player in the game optimises its payoff by adjusting the transmission power so as to enhance the overall performance in terms of throughput, handover, energy consumption, and load balancing. In order to choose the preferred transmission power for each player, the payoff function takes into account the gain of increasing the transmission power, energy consumption, base station load, and unnecessary handover. The cell selection is performed using the technique for order preference by similarity to an ideal solution (TOPSIS). A game theoretical approach is implemented and evaluated for dense small cell heterogeneous networks to validate the enhancement achieved in the proposed method. Results show that the proposed game theoretical approach provides a throughput enhancement while reducing the power consumption in addition to minimise the unnecessary handover and balance the load between base stations.

Enhancing QoS in 5th generation Het-Net via synergistic TVWS spectrum sharing for distributed adaptive small cells

5G mobile network is expected to support an exploding traffic demand by densifying the network with heterogeneous deployment of multiple cells. Such dense network in LTE band results in spectrum scarcity and leads to high system interference. This brought in the necessity to incorporate Dynamic Spectrum Access (DSA) to additional bands for communication. The possibilities of utilizing TV White Spaces (TVWS) in cellular communication is recently considered by many researchers. However, considering the spatio-temporal variability and quality of available channels among these white spaces, there exists certain design challenges. In this paper, we present a solution to the spectrum shortage for 5G, by making the small cells in Heterogeneous Network (Het-Net) operate in TVWS. A channel detection model is developed, for identifying the available TVWS, by considering both channel sensing and geo-location database access. This improves the system accuracy. Further, a synergistic algorithm is developed, that assigns sub channels to mobile users by determining an optimum transmission power level for every mobile user on its allocated sub channel, such that it satisfies the required interference and minimum data rate constraints. The problem of jointly selecting the optimal sub channel as well as the transmission power level is formulated as a Mixed Integer Non-Linear Programming problem. The formulated problem is proved to be NP Hard, which motivated the utilization of a numerical heuristic approach to solve the problem. A Geometric WaterFilling algorithm with Sub Channel Peak Power and Multi-user Interference Constraint (GWSPI) is developed, that solves the problem in reduced time, while achieving optimal results. A detailed complexity analysis and extensive simulation analysis of the proposed method is executed to prove its efficiency compared to some standard approaches. The proposed approach shows improvement in terms of interference management and transmission power control, giving a near optimal result in real life scenario.

Caching for Heterogeneous Small-Cell Networks With Bandwidth Allocation and Caching-Aware BS Association

This letter studies the caching problem for heterogeneous small-cell networks with bandwidth allocation and caching-aware base station association. The caching control and bandwidth allocation problem aims at minimizing the request miss ratio. To solve this problem, we propose a line-search-based-iterative (LSBI) algorithm which determines the solution by combining the line-search algorithm to obtain the optimal bandwidth allocation with the iterative caching algorithm to acquire a caching solution. Numerical results demonstrate that the LSBI algorithm significantly outperforms existing caching algorithms, and is on a par with a performance bound.

UFMC-Based Interference Management for Heterogeneous Small-Cell Networks

In this paper, we apply the advanced waveform universal filtered multi-carrier (UFMC) to a heterogeneous small-cell network, where both the inner-cell interference and the inter-cell interference are considered. By employing UFMC, a dynamic spectrum allocation scheme and a precoding scheme are proposed to mitigate the interference. The proposed UFMC based interference management schemes are analyzed in detail, and the achievable data rate of the proposed scheme is derived. Simulation results show that the proposed UFMC based schemes can efficiently mitigate the interference, and reduce the effect of frequency offsets.