Number Of Small Base Stations Research Articles

Energy and spectrum efficient cell switch-off with channel and power allocation in ultra-dense networks: A deep reinforcement learning approach

The high density of small cells in the Ultra-Dense Network (UDN) has increased the capacity and the coverage of Fifth Generation (5G) cellular networks. However, with increasing the number of Small Base Stations (SBSs), energy consumption rises sharply. One suggested method to reduce energy consumption is to manage the SBS On/Off switching. Moreover, due to spectrum constraints, the Power Control and Resource Allocation (PCRA) are other significant issues in UDN, which affect the Energy Efficiency (EE) and the Spectrum Efficiency (SE). Recent works in UDN have not presented the optimal SBSs On/Off switching and PCRA technique simultaneously to maximize the EE and the SE while ensuring Quality of Service (QoS) requirements of User Equipments (UE). In this paper, a distributed method based on a multi-agent Deep Q-Network (DQN) is proposed to deal with the mentioned challenges simultaneously. Therefore, each SBS can learn a policy for managing On/Off switching and downlink PCRA using two DQNs. The proposed method seeks to optimize the EE and the SE as well as guarantee the minimum required data rate of UEs. Simulation results show that the proposed method improves the EE and the SE compared to previous solutions. Furthermore, unlike previous distributed approaches that use the UEs as learning agents, the proposed method uses the SBSs as agents. Thus, the signaling overhead and computational complexity of the UEs decrease.

Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors’ knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.

Energy-Efficient Resource Allocation for NOMA-MEC Networks With Imperfect CSI

The combination of non-orthogonal multiple access (NOMA) and multi-access edge computing (MEC) can significantly improve the system performance including communication coverage, spectrum efficiency, etc. In this article, we focus on energy-efficient resource allocation for a multi-user multi-BS NOMA-MEC network with imperfect channel state information (CSI), where each user can upload its tasks to multiple base stations (BSs) for remote executions. We propose an optimization scheme, including task assignment, power allocation and user association, to minimize energy consumption. Specifically, we transform the probabilistic problem into a non-probabilistic one. To efficiently solve this nonconvex energy minimization problem, we first investigate the one-user two-BS case and derive the optimal closed-form expressions of task assignment and power allocation via the bilevel programming method. Subsequently, based on the derived optimal solution, we propose a low complexity algorithm for the user association in the multi-user multi-BS scenario. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme and the identical results with lower complexity from the exhaustive search with the small number of BSs.

A QoS-based joint user association and resource allocation scheme in ultra-dense networks

Ultra-dense networks (UDNs) have become an important architecture for the fifth generation (5G) networks. A large number of small base stations (SBSs) are deployed to provide high-speed and seamless connections for users in the network. However, the advantage of increasing the system capacity brought by the dense distribution of SBSs comes at the cost of severe inter-cell interference. Although the user-centric virtual cell method has been proposed to solve the interference problem, some challenges have been encountered in practical applications. For example, inter-cell interference still exists to a certain extent, and the cell load may be imbalance. Hence, under the virtual cell architecture, we propose a quality of service (QoS)-based joint user association and resource allocation scheme in UDNs. In order to mitigate the interference, balance cell load and improve the system throughput, a non-convex NP-hard problem is formulated. To effectively solve this problem, we decouple the formulated problem into three sub-problems: user association, physical resource block (PRB) allocation and power allocation. First, we consider the QoS requirements of user equipment (UE) and perform user association based on the PRB estimation method. Then, based on the overlapped virtual cells constructed, we propose a graph-based PRB allocation scheme for reducing virtual inter-cell interference. Moreover, we solve power allocation sub-problem by using the difference of concave (DC) programing method. The simulation results show that our proposed scheme is superior to other schemes in terms of user rates, cell load and system throughput.

Research on Network Resource Optimal Allocation Algorithm Based on Game Theory

This paper briefly introduced the structure of heterogeneous cellular network and two algorithms which are used for optimizing the network resource allocation scheme: dynamic game algorithm based on spectrum allocation and the game allocation algorithm based on power allocation and alliance. After that, the two algorithms were simulated in MATLAB software and compared with another power iterative allocation algorithm based on non-cooperative game. The results showed that the system energy efficiency of the three algorithms decreased with the increase of the number of small base stations in the network; with the increase of the number of users in the network, the system energy efficiency of the three algorithms increased; under the same number of small base stations or users, the system energy efficiency of the allocation scheme based on the power allocation and alliance was the highest, followed by the power iterative allocation algorithm and the spectrum allocation based game algorithm; the allocation scheme based on power allocation and alliance could provide users with more satisfactory and fair services.

Location Privacy-Preserving Distance Computation for Spatial Crowdsourcing

Data privacy, especially location privacy, is paramountly important for protecting individual's information in smart cities in the big data era. One of the examples is in spatial crowdsourcing (SC). It enables people not only to issue spatiotemporal tasks to ask for help as requesters but also to solve others' tasks as workers on the SC platform. While SC brings convenience to people, it also produces severe location privacy problems, which have been recently paid more attention from both academia and industries. In this article, we address the location privacy problem in SC in a practical and secure way. We propose a location privacy-preserving framework for almost all existed mainstream distance computations in the SC system, namely, Euclidean-L3P, Minkowski-L3P, Manhattan-L3P, and Chebyshev-L3P, among which the first two are constructed based on homomorphic encryption and composite-order multilinear mapping while the latter two on the homomorphic encryption and prefix membership verification approach. Location privacy is resolved because of the above techniques having enabled that all distance computations are evaluated through ciphertexts without disclosing any location information. Security analysis shows that our framework can prevent a strong adversary from obtaining participants' location privacy. Performance analysis evaluates computation and communication overheads between protocols. The results show that Euclidean-L3P is more efficient than Manhattan-L3P and Chebyshev-L3P in terms of computation overheads when the SC applications require a small number of participants, a large plaintext space, and a small number of base stations. Moreover, compared with Manhattan-L3P and Chebyshev-L3P, Euclidean-L3P is a better choice in terms of communication overhead.

The analysis of coverage probability, ASE and EE in heterogeneous ultra-dense networks with power control

The ultra-dense network is a promising technology to increase the network capacity in the forthcoming fifth-generation (5G) mobile communication networks by deploying lots of low power Small Base Stations (SBSs) which overlap with Macro Base Stations (MBSs). The interference and energy consumption increase rapidly with the number of SBSs although each SBS transmits with small power. In this paper, we model a downlink heterogeneous ultra-dense network where a lot of SBSs are randomly deployed with MBSs based on the Poisson point process. We derive the coverage probability and its variance, and analyze the area spectral efficiency and energy efficiency of the network considering three Fractional Power Control (FPC) strategies. The numerical results and Monte Carlo simulation results show that power control can mitigate the interference and balance the performances of inner-user and edge-user equipments. Especially, a great improvement of energy efficiency is archived with a little loss of area spectral efficiency when FPC is adopted. Finally, we analyze the effect of base stations’ (BSs’) sleeping on the performance of the network when it is partially loaded.

Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks

This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand and inter-SBS interference. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By exploiting mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. The simulation results also show that the proposed caching algorithm is robust to imperfect popularity information, while ensuring a low computational complexity.

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.

Joint User Association and Beamforming Design for Millimeter Wave UDN With Wireless Backhaul

As millimeter wave (mmWave) beamforming (BF) system can increase the areal capacity in ultra-dense network (UDN), a joint design of user association and hybrid BF is challenging due to the high feedback overhead and complicated interference management where many communication nodes use large antenna arrays. In addition, the wireless backhaul should be incorporated because it allows a flexible design, while the wired backhaul is difficult to be installed as the number of small base stations increases. To integrate the wireless backhaul and access, the time resource partitioning between the backhaul and access transmissions is needed, which affects the network performance combined with the user association and BF design. In this regard, this paper deals with the joint problem of the time resource allocation, user association, and BF design to maximize the weighted sum rate. Then a two-stage design approach is proposed based on the limited channel feedback to reduce the complexity and overhead. Simulation results show that the proposed limited feedback hybrid BF scheme outperforms the baseline schemes. In addition, we find that the maximum spatial multiplexing gain can be achieved if the number of antennas of each node increases with an order of larger than the square of the number of RF chains. At the same time, the multi-node diversity gain can be obtained in mmWave UDN, which depends on the path loss exponent of line-of-sight.

Joint optimisation of radio and infrastructure resources for energy‐efficient massive data storage in the mobile cloud over 5G HetNet

This study formulates the problem of radio resource and data transmission strategy in a dense HetNet enhanced with mobile cloud computing and mobile edge computing technologies as a combinatorial optimisation problem. An ant colony optimisation (ACO)-based energy-aware algorithm is proposed as solution approach, which consists of a data transmission management scheme and a physical resource blocks/bandwidth allocation scheme. Simulation results show that compared with baseline approaches [(greedy and data split multiple user equipment (UEs) algorithms]), the proposed ACO-based algorithm achieves: (i) about 99% of reduction in terms of transmission delay under varying data file sizes (resp. varying number of UEs in the network); (ii) about 99% of reduction in terms of transmission energy dissipated by an UE during transmission of a file under varying data file sizes (resp. varying number of UEs in the network); (iii) improved performance with an increase in the number of small base stations in the network, (iv) significant reduction in the energy transmitted (by the UE) during transmission of big data files to the cloud storage; and (v) some stability irrespective of the variation in network conditions such as increasing the number of active UEs or increasing the number of UEs in the network.

Joint Power and Bandwidth Allocation for Energy-Efficient Heterogeneous Cellular Networks

This paper investigates the problem of energy efficiency maximization (EEM) for the small cells that coexist with a macro cell in an underlay heterogeneous cellular network, where a macro base station and a number of small base stations transmit signals to a macro user and small users through their shared spectrum. We propose a joint power and bandwidth allocation (JPBA) scheme for the sake of maximizing the energy efficiency (EE) of small cells under the constraint of a guaranteed quality-of-service requirement for the macro cell. Considering that our formulated EEM-based JPBA (EEM-JPBA) problem is non-convex, we convert the original fractional problem into an equivalent subtractive form by adopting the Dinkelbach’s method, which is addressed through the augmented Lagrange multiplier approach. Moreover, a new two-tier iterative algorithm is presented to obtain the optimal solution of our EEM-JPBA scheme. Simulation results demonstrate that the proposed two-tier iterative algorithm can quickly converge to the optimal EE solution. In addition, it is shown that the proposed EEM-JPBA scheme significantly outperforms the conventional power and bandwidth allocation methods in terms of their EE performance.

Joint Subcarrier Assignment and Global Energy-Efficient Power Allocation for Energy-Harvesting Two-Tier Downlink NOMA Hetnets

In this paper, the problem of joint subcarrier assignment and global energy-efficient power allocation (J-SA-GEE-PA) for energy-harvesting (EH) two-tier downlink non-orthogonal multiple-access (NOMA) heterogeneous networks (HetNets) is considered. Particularly, the HetNet consists of a macro base-station (MBS) and a number of small base-stations (SBSs), which are solely powered via renewable-energy sources. The aim is to solve the J-SA-GEE-PA maximization problem subject to quality-of-service (QoS) per user as well as other practical constraints. However, the formulated J-SA-GEE-PA problem happens to be non-convex and NP-hard, and thus is computationally-prohibitive. In turn, problem J-SA-GEE-PA is split into two sub-problems: (1) subcarrier assignment via many-to-many matching, and (2) GEE-maximizing power allocation. In the first sub-problem, the subcarriers are assigned to users via the Gale-Shapley deferred acceptance mechanism. As for the second sub-problem, the GEE-PA problem is solved optimally via a low-complexity algorithm. After that, a two-stage solution procedure is devised to efficiently solve the J-SA-GEE-PA problem, while ensuring stability. Simulation results are presented to validate the proposed solution procedure, where it is shown to efficiently yield comparable network global energy-efficiency to the J-SA-GEE-PA scheme, and superior to that of OFDMA; however, with lower computational-complexity. The algorithmic designs presented in this work constitute a step towards filling the gap for computationally-efficient and effective resource allocation solutions to guarantee a fully autonomous and grid-independent operation of EH two-tier downlink NOMA HetNets.

Optimized sleep strategy based on clustering in dense heterogeneous networks

As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively.

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.

Signal strength and spectral efficiency in heterogeneous ultradense networks

AbstractTo meet the rapid data growth requirements of mobile users, heterogeneous ultradense networks improve the spatial reuse efficiency of spectrum resources through the ultradense deployment of small base stations (SBSs) in a given area in the forthcoming fifth‐generation mobile communication networks. It has become one of research hotspots that solve this problem in the recent years. Using the stochastic geometry, the cumulative distribution function and the probability density function of distance from user to its kth closest SBS are derived in this paper. Furthermore, the means of received signal strength from the users' kth closest base station and the mean of signal‐to‐interference‐plus‐noise ratio are derived. The expression of the relationship between spectral efficiency and the density of the SBSs is presented. Finally, The results show that the signal‐to‐interference‐plus‐noise ratio is inversely proportional to the density of SBSs in the 1‐tier ultradense networks. In the 2‐tier heterogeneous ultradense networks, the spectral efficiency of the base station increases first and then decreases with the increasing of the density of SBSs. Moreover, allocating more spectrum resource to SBSs will increase the area spectral efficiency when the number of SBS is far more than the number of macro base stations.

Cluster-Based Cooperative MIMO-OFDMA Cellular Networks: Scheduling and Resource Allocation

In this work, a cross-layer framework is proposed for scheduling and resource allocation in the context of cooperative wideband wireless cellular systems supporting heterogeneous traffic. To this end, the network is partitioned into clusters, each comprising a small number of base stations that share data and/or control information and thus are able to apply cluster-wide cooperative multiple-antenna techniques such as network MIMO (N-MIMO) or large-scale MIMO (LS-MIMO). In contrast to previous works whose main goal was the study of purely physical layer metrics, the framework proposed here serves to evaluate the network performance in terms of operator-relevant metrics such as the average user throughput, the average delay, or different fairness indices. The cross-layer design introduced here is general enough to encompass various forms of power allocation or scheduling policies while being able to incorporate per-cluster or per-base power constraints. Remarkably, different greedy algorithms are introduced for both N-MIMO and LS-MIMO that effectively exploit the multiuser and frequency diversity of the system while remaining computationally feasible. A particularly interesting application of the proposed framework, explored in detail in this paper, is a thorough and realistic comparison of the performance of N-MIMO and LS-MIMO that reveals the strengths and weaknesses of each strategy in terms of different performance metrics.

Overview of CoMP Clustering in UDN

System throughput and resource utilization can be improved in ultra-dense networks (UDN) by deploying a large number of small base stations. However, base stations are very close in UDN, which will cause serious interference between base stations. To solve this problem, coordinated multi-point (CoMP) has been improved, which has the ability to reduce inter-cell interference and to improve data rate. However, the complexity of cooperation among all cells is very high. Therefore, it is necessary to construct small-size cooperation clusters. This article analyzes one of the key challenges of CoMP application in UDN—optimal CoMP clustering. Firstly, the background knowledge of CoMP is introduced. Secondly, we focus on the classification and summarization of the latest achievements in clustering research and the objective and challenges faced in the design of CoMP clusters. Finally, the future research directions and potential methods of CoMP clustering are discussed.

Real-world traffic analysis and joint caching and scheduling for in-RAN caching networks

This paper analyzes the traffic of a current LTE network in China and investigates the joint optimization of content object caching and scheduling for in-radio access network (RAN) caches. Cooperative caching has been well recognized as a way of unleashing the ultimate potential of in-RAN caches, yet its feasibility is still unexplored. Moreover, content object caching and scheduling are two key issues for cache deployment, which are usually jointly considered and resolved. However, they are triggered by different events with different time granularities. Therefore, on the basis of the real-world dataset, the feasibility of in-RAN cooperative caching is proved from aspects of network topology, traffic load difference among small base stations (SBSs) and correlation analysis of content objects requested at different SBSs. Then, it is verified that different time scales should be considered in making content object caching and scheduling decisions. To exploit in-RAN cooperative caching while meeting the time scale requirement in making caching and scheduling decisions, an optimization problem is constructed considering practical transmission constraints in wireless and backhaul. It is proved to be a quadratic assignment problem, and then, a joint caching, and wireless and backhaul scheduling algorithm is proposed based on Lagrangian relaxation and decomposition, and hastening branch and bound. The performance of the proposed algorithm is evaluated based on the real-world dataset. Results depict the relationship among the cache capacity, the number of SBSs, the connection probability of SBSs and the objective performance, and show that the proposed algorithm can achieve better performance, compared with the existing algorithms.

Small Base Station Association and Cooperative Receiver Design for HetNets via Distributed SOCP

How to determine the right number of small base stations to activate in multi-cell uplinks to match traffic from a fixed quantity of K users is an open question. This paper analyses the uplink cooperative that jointly receives base stations activation to explore this question. This paper is different from existing works only consider transmitting power as optimization objective function. The global objective function is formulated as a summation of two terms: transmitting power for data and coordinated overhead for control. Then, the joint base stations activation and beamforming problem is formulated as a mixed integer second order cone optimization. To solve this problem, we develop two polynomial-time distributed methods. Method one is a two-stage solution which activates no more than K small base stations (SBSs). Method two is a heuristic algorithm by dual decomposition to MI-SOCP that activates more SBSs to obtain multiple-antennae diversity gains. Thanks to the parallel computation for each node, our methods are more computationally efficient. The strengths and weaknesses of these two proposed two algorithms are also compared using numerical results.