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Small cells (SCs) are expected to be ultra-densely deployed in or close to the traffic hot-spots in the fifth generation (5G) mobile networks to provide wireless capacity cost-effectively. Traffic hot-spots change over time, which means SCs cannot be deployed in a one-off manner as macrocells normally do, rather they should be constructed in a staged process. Hence, mathematical models that capture the time-varying staged-construction process, are urgently needed for operators to effectively predict the construction period, but are currently lacking. In this paper, inspired by the foam bursting process-a natural phenomenon that can be observed in daily life such as hand-washing, we first propose a novel model that can predict the time-varying expectation and logarithmic variance of SC coverage areas. Then, we verify the model by real network deployment cases. Additionally, in order to extract parameters from historical base station deployment data, a parameter estimation algorithm is designed and verified. The findings of the paper reveal that mobile operators should construct ultra-dense SC networks in a staged manner like how larger foams split into smaller ones.

Since the dawn of mobile communication systems, reducing the cell size has been one option to increase the signal-to-interference-plus-noise ratio (SINR) in both links. The impact of this reduction can be perfectly understood by considering Shannon’s law. This work studies in detail the performance of Home eNBs (HeNBs), nodes with a smaller coverage area. After a detailed theoretical study of the SINR, a simulation approach is used to extract performance results in small cell indoor scenarios. Results corresponding to the goodput, delay and packet loss ratio are analyzed. Based on an improved version of LTE-Sim, the proportional fair, frame level scheduler (FLS) and exponential rule are tested in an indoor environment. With the saturation conditions taken into consideration, the FLS performs better than the other schedulers. This work shows that with the considered applications, it is possible to achieve a reduction in the transmitter power of HeNBs without compromising the small cell network performance., This work was supported by Foundation for Science and Technology/Ministry of Science, Technology and Higher Education (FCT/MCTES) through national funds and, when applicable, co-funded EU funds under the project UIDB/50008/2020, COST CA 15104 Inclusive Radio Communication Networks for 5G and Beyond (IRACON), Optical Radio Convergence Infrastructure for Communications and Power Delivering (ORCIP, 22141-01/SAICT/2016), TeamUp5G and CONQUEST (CMU/ECE/0030/2017). The TeamUp5G project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie project number 813391.

In the next generation wireless communication systems, high-speed data rate, reliable link quality, and ubiquitous access are necessary requirement. To meet the next generation communication system requirements, the ultra-dense small cell network (SCN) is proposed as one of the possible candidate solutions. To achieve high data rate and reliable link quality, coordinated multiple point (CoMP) transmission is usually used in ultra-dense SCN to satisfy the performance target. However, to realize CoMP transmissions in ultra-dense SCN, the feedback load is heavy because of the large amount of small cells and users. In this study, we want to investigate the ultra-dense SCN environment and design an effective method for its downlink (DL) transmission. This method consists of iterative scheme which iteratively solves the received signal with proper step-size value $\gamma $ . The step-size value $\gamma $ is very important to the convergence performance of iterative scheme because it affects the convergence speed and the converged error level of the iterative algorithm. Therefore, in this paper, we propose a novel machine learning based method which creates data model from input data. When the model is well established, the optimal step-size can be well estimated and the feedback information can become rough and the bandwidth allocated for feedback can be saved for data transmission. The simulations show that, the proposed method can create good model and achieve better convergence performance. For example, about the distribution of the transmissions with convergence iteration number less than $10^{4}$ level, the proposed method can obtain 30% improvement than the traditional method with fixed step-size $\gamma =0.01$ .

In dense small cell networks (DSCNs), small cells are heterogeneous due to the unplanned deployment and various traffic loads, resulting in different energy efficiency (EE) preferences. In this paper, taking into account the heterogeneity of small cells, energy saving (ES) and EE are jointly optimized through subchannel allocation, subframe configuration and power allocation. In order to quantify the effects of small cells’ heterogeneous information on EE, an EE preference function is first defined. Then, the joint ES and EE optimization problem is formulated as a multi-objective optimization problem. Due to the coupling of ES and EE, obtaining the solution is non-trivial. Therefore, we propose a heterogeneity-aware ES and EE (HESEE) optimization algorithm, where subchannel allocation is optimized to ensure the fairness of active subframes required by the users in the same small cell base stations (SBSs). Then subframe configuration is conducted via group formation sleep mechanism. Particularly, address the non-concave sum-of-ratios optimization for system EE, the concave-convex procedure (CCCP) method is adopted. Simulation results show that the proposed HESEE algorithm can optimize the SBSs’ EEs according to their EE preferences. In addition, the HESEE algorithm can achieve good performance in reducing energy consumption as well as improving the system EE.

In the fifth generation (5G) communication system, maximizing energy utilization is one of the key challenges especially with limited green energy sources. In particular, it is important to determine the optimal transmission policy for the hybrid model with grid and green energy in small cell networks. The optimal transmission policy should minimize both grid power consumption and wastage of green harvested energy at the same time; while satisfying the quality of service (QoS) requirements such as minimum packet drop probability. In contrast to existing policies with a single objective for the hybrid powered small cell networks, the proposed transmission policy adopts a multi-objective model checking for Markov decision process (MOMC-MDP) with a linear temporal logic (LTL) scheme. The MOMC-MDP firstly determines all feasible decisions for packet transmissions in every time slot, and then chooses one of the best feasible decisions to obtain the optimal transmission policy. Three dimension Markov decision process (3D-MDP) that includes packet buffer, battery capacity, and channel state, are used with the age of information (AoI) of the packet arrivals to improve the overall performance of the proposed MOMC-MDP scheme. Numerical results validate that the proposed MOMC-MDP with AoI can provide higher green energy utilization compared to the conventional MDP-based schemes.

This paper investigates a satellite-terrestrial backhaul framework to enhance efficient data offloading for heterogeneous terminals, including delay-sensitive and delay-tolerant users. In the considered architecture, ground terminals in satellite-terrestrial small cells can access different services via the satellite-terrestrial station (STS) in each cell. The satellite offloads the requested services to corresponding STSs, and each STS provides services to terrestrial terminals via an OFDM-based downlink system. We aim to maximize the sum throughput of all small cells while integrating joint satellite backhaul power allocation and STS downlink resource allocation. The problem is firstly decomposed into two types of subproblems by decoupling the optimization of satellite backhaul capacity and downlink capacity in small cells. Then, to satisfy users' delay requirements, the downlink STS throughput is maximized over multiple slots, and we propose a two-step algorithm to schedule users during these slots. By taking advantage of a delay-violation parameter, the algorithm iteratively approaches the optimal power and subchannel solution, while guaranteeing the delay requirements. Moreover, to reduce the computational complexity, we propose a greedy-based sub-optimal scheduling algorithm where delay requirements are guaranteed by users' self-search for favorable resources, aiming at sacrificing the minimum throughput in exchange for the delay performance. Simulation results show our algorithms effectively improve the throughput performance while ensuring the delay constraints, maintaining a well-performed balance between throughput and delay performance.

Ultra-dense small cell networks (UD-SCNs) will be an integral part of next generation network (NGN). How to deal with serious interference is one of the important challenges in a UD-SCN. In this paper, the cooperative interference management problem for a UD-SCN is explored by allowing small cell base stations (SBSs) to collaborate with their neighbors. The proposed coalitional structure generation among SBSs can mitigate the co-tier interference within a coalition, thus improving the network capacity. Specifically, a cooperative scheme among the neighboring SBSs is formulated as a coalitional structure generation with characteristic forms. Furthermore, the relative sub-channel resources are allocated in the process of cluster generation. Compared with the existing SBS cooperative schemes, the novelty of the proposed SBS cooperation method, which uses a hierarchical clustering algorithm (SC-HCA) to compute the pairs of members, is demonstrated. The computation can enhance the efficiency of the proposed algorithm and is especially suitable for UD-SCN scenarios with tens and even hundreds of cells. The simulation results show that the proposed SC-HCA achieves a 422.13% system data rate improvement relative to that of the non-cooperative scheme in a UD-SCN scenario.

The use of millimeter wave technologies offer a promising solution for dense small cell networks, despite having to contend with challenging propagation characteristics. In particular, user-induced effects can lead to significant channel variations depending on the user equipment (UE) usage mode which in turn, can impact the quality of service. Estimation of UE operating conditions is therefore critical for optimal radio resource management. We propose a new approach to user activity recognition which makes use of both supervised and unsupervised machine learning. In particular, using information extracted from the received signal strength (RSS), a common metric readily available from many receiver chipsets, we perform a classification of user state ( static or mobile relative to an access point) and UE mode of operation ( voice call, using an app or in pocket ). To develop and then train our classification system, measured RSS data was obtained using a custom 60 GHz measurement system for a range of indoor office scenarios which considered various UE to ceiling mounted access point configurations. In our approach, differentiation between static and mobile states is performed in preprocessing using a k-means algorithm. Small-scale fading features are then estimated from the RSS data and, using different feature scaling mechanisms, various supervised learning approaches are applied to investigate the optimal classification accuracy for the considered use cases in this work. We compare the classification performance of various window sizes and types, and show that a sliding window length of 1s without overlap performs best for time series segmentation at 60 GHz for the activities considered in this study. Among the different supervised learning approaches, the Decision Tree (DT) classifier performs best for both the user static and mobile cases with an accuracy of 100% and 98.0%, respectively. For static cases, user orientation, i.e., line-of-sight (LOS), quasi-LOS, and non-LOS, can also be classified and here the DT classifier also performs best with an accuracy of 98.2%, 97.6% and 100% for the voice call , using an app or in pocket use cases. Additionally, a feature ranking algorithm, called ReliefF , is adopted to determine the small-scale fading features that have the most significant influences on the classification accuracy and three different feature sets, namely Full , Reduced and Constrained sets, are then proposed based on feature ranking results. This allows the proposed techniques to be deployed on wireless platforms with different levels of processing capability.

A software-defined networking (SDN) architecture is capable of integrating all radio frequency and optical wireless small cell networks (e.g. fifth generation (5G), long-term evolution (LTE) femtocell, wireless fidelity (WiFi), light fidelity (LiFi)) in one network domain. This paper considers a SDN-enabled heterogeneous network (HetNet) comprised of LiFi, LTE femtocell and WiFi access points (APs). The HetNet control plane maintains the state of the network topology and wireless resources, which can support the development of intelligent service provisioning and efficient data communications in x generation (xG) wireless networks. The SDN applications use the network state to provide services in the data plane. However, when the state of network and wireless resources constantly changes, the SDN applications cannot provide reliable and guaranteed services to the wireless user equipments. This paper develops a queuing theoretic framework, which provides a performance evaluation for the SDN-enabled HetNet and applications convergence. A traffic engineering (TE) scheme is developed to support dynamic agnostic downlink flows routing to APs and differentiated granular services across the HetNet. Network and user centric policies are developed to make applications aware of network resource availability on the northbound and southbound interfaces of a SDN controller. Numerical models are introduced to study the impact of the computation and communication resources of northbound and southbound interfaces on the SDN-enabled HetNet scalability and the quality-of-service (QoS) guarantee of applications. Also, simulation scenarios are conducted to evaluate the performance of the TE scheme in provisioning effective and reliable services for subscribers.

In future cellular networks, performance target includes not only increasing data rate, but also reducing latency. The current LTE-Advanced systems require four message exchanges in the random access and uplink transmission procedure, thus inducing high latency. In this paper, we propose a 2-way random access scheme which effectively reduces the latency. The proposed 2-way random access requires only two messages to complete the procedure at the cost of increased number of preambles. We study how to generate such preambles and how to utilize them. According to extensive simulation results, the proposed random access scheme significantly outperforms conventional schemes by reducing latency by up to 43%. We also demonstrate that computational complexity slightly increases in the proposed scheme, while network load is reduced more than a half compared to the conventional schemes.

The millimeter wave (mmWave) frequency band will become a key enabler for future wireless systems currently facing the explosive growth of data traffic and the sparsity of the traditional ultra-high frequency (UHF) band. Nevertheless, the challenges for mmWave communications lie in high propagation loss, sensitivity to blockage, high cost for equipping directional antennas, and so on. Furthermore, the traditional design for the UHF networks cannot be directly used for future mmWave networks, which needs to be reshaped from the perspectives of fundamental objectives, various constraints, and different degrees of freedom. This paper addresses the key functions and discusses the challenges for PHY layer and MAC layer design in mmWave small-cell networks, including mmWave antenna design, beamforming, initial access, radio resource allocation, power allocation, and so on. The novel resource management approach for the joint PHY-MAC layer design is proposed to find the trade-off among hardware cost of the mmWave antenna, beamforming overhead, and efficiency of the new resource block (RB) allocation in beam-frequency-time (B-T-F)-dimension. © 2013 IEEE.

The network traffic of mobile wireless communication grows exponentially in recent years. In order to serve intense traffic areas, such as train stations, airports, or hot-spot buildings, the investigation on 5G networks for the next-generation mobile telecommunications has been triggered. One promising solution is the dense small cell networks which consist of a large number of small cells. Base stations (BSs) are one of the major contributors to the power consumption in cellular networks. To address the power consumption issue, the BS ON/OFF control mechanism is proposed by the 3GPP. Some previous solutions for BS ON/OFF control try to save energy by switching off as many BSs as possible. In this paper, we aim to minimize the total power consumption of a dense small cell network while offering QoS guarantees to all its user equipment (UEs). The BS ON/OFF control for minimal total power consumption of an entire network is an NP-hard problem. To this problem, we propose a clustering-based scheme for BS ON/OFF control which in the past was applied to mitigate interference. The proposed scheme is a three-phase approach, consisting of BS clustering algorithm, intra-cluster BS ON/OFF control algorithm, and inter-cluster BS ON/OFF control algorithm. By the clustering technique, we reduce an NP-hard problem to a number of manageable sub-problems. For the possibility of further power saving, we also consider the ON/OFF control of BSs residing on the boundaries of clusters. The time complexity is analyzed. Finally, experimental results reveal that the proposed scheme outperforms existing approaches in power saving and is capable of retaining QoS guarantees for all UEs.

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.

Deployment of small cells was introduced to support high data rate services and expand macro cell coverage for the envisioned 5G networks. A small cell network, which has a smaller size, along with the user equipment (UE) mobility, frequently undergoes unbalanced load status. Consequently, the network performance is affected in terms of throughput, increasing handover failure rate, and possibly higher link failure rate. Hence, load balancing has become an important part of recent researches on small cell networks. Mobility Load Balancing (MLB) involves load transfer from an overloaded small cell to under-loaded neighbouring small cells for the more load-balanced network. This transfer is performed by adjusting the handover parameters of the UEs according to the load situations of the small cells in the vicinity. However, inaccurate adjustment of parameters may lead to inefficient usage of network resources or degrade the Quality of Service (QoS). In this paper, we introduce a Utility-based Mobility Load Balancing algorithm (UMLB) and a new term named load balancing efficiency factor (LBEF). The UMLB algorithm considers the operator utility and the user utility for the MLB-based handover process. While LBEF is proposed to order the overloaded cells properly for the MLB algorithm operation. The simulation results show that the UMLB minimizes standard deviation with a higher average-UE data rate when compared to existing load balancing algorithms. Therefore, a well-balanced network is achieved.

Device-to-device (D2D) communication with increased spectral efficiency and reduced communication delay has undoubtedly become a general trend in future wireless networks. However, when D2D communication is incorporated into small cell networks (SCNs) with large number of randomly overlapped small cells, the co-channel interference between small cell users (SUEs) and D2D users is an inevitable challenge, especially with the heterogeneous spectrum, i.e., licensed spectrum bands and unlicensed spectrum bands. In this paper, we study the downlink channel allocation in D2D-assisted small cell networks with heterogeneous spectrum bands. By taking the required data rate of users and the interference constraint of SUEs into account, we formulate a channel allocation problem integrating channel selection and channel sharing to maximize the network utility, which is the service satisfaction of all users. To derive the solution, we decompose the optimization problem into two games: a potential game and a coalition game. Then, a potential game-based scheme using an interference graph and a coalition scheme with D2D user transferring is proposed to solve these two games, respectively. Based on these schemes, a two-stage distributed channel allocation algorithm is proposed and can converge with low computational complexity. Moreover, the simulation results reveal that the proposed algorithm could achieve high system throughput and network utility.

In this paper, we develop a novel wireless backhauling strategy for small-cell networks based on dynamic base station (BSs) cooperation, which we call the fog-radio access network (F-RAN) backhauling strategy. By taking advantage of fog-computing, our proposed strategy enables BSs to combine and process signals received from diverse paths, which can significantly increase the transmission efficiency of the backhaul network. We first model an F-RAN-enabled network and three existing backhauling strategies, namely, direct transmission , decode and forward , and cloud-RAN . We then analyze and compare the performance of these strategies. The numerical results show that our proposed strategy provides the highest throughput for cell edge users while maintaining the same performance in most of the other areas. Moreover, for dense small-cell networks with poor backhaul channels, F-RAN outperforms all other strategies.

In ultra-dense small cell networks, interference mitigation is very important due to severe interference. Interference dynamics caused by time-varying environment should be aware and characterized when an interference-aware power control policy is designed to mitigate interference. Meanwhile, interference perception should not be naturally assumed to have complete information with certainty. Generally, it is known that a generic player will react to all the players actions and states in a power control game, which involves huge interference-related information exchange with dynamics and uncertainties. Therefore, to reduce requirements of complete information, we formulate a robust power control mean field game taking the uncertainties of both state dynamics and cost functions into consideration. To achieve the robust power control, we regard the power control problem as a game with players whose individual states are combined by a disturbance term and a Brownian motion. We derive the robust Fokker-Planck-Kolmogorov and Hamilton-Jacobi-Bellman equations, and based on which we propose the robust interference-aware power control algorithm. Simulation results demonstrate the improved performance and the robustness of the proposed algorithm.

This paper considers a cognitive small cell network, where one cognitive base station (CBS) transmits information to the cognitive user and energy to the energy harvesting receivers (EHRs). The Markov channel model is exploited to characterize the state change of the macrocell base station. The spectrum sensing time, the spectrum sensing interval, and the beamforming matrixes of the CBS are jointly optimized to achieve three goals: the maximization of the CBS throughput, the minimization of the energy cost of the CBS, and the minimization of the interferences to the macrocell users (MUEs). These objectives are optimized subject to the interference constraints of the MUEs, the secrecy rate constraint, the transmit power constraint of the CBS, and the energy harvesting constraints of the EHRs. The formulated problems are challenging non-convex and difficult to solve. A 1-D line search method and semidefinite relaxation-based algorithm is proposed to solve these problems. It is proved that the optimal solution can be obtained under some conditions. If the conditions are not satisfied, Gaussian randomization procedure is used to obtain the suboptimal solutions. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme.

In this paper, by jointly considering resource reuse and allocation, we formulate a stochastic optimization programming to investigate the spectral efficiency maximization problem in dynamic wireless self-backhaul small cell networks, where random and finite-traffic loads are considered while keeping the system stable. Then, by leveraging the fractional programming theory and the Lyapunov optimization technique, an extremely simple but optimal queue-aware dynamic resource reuse and joint allocation algorithm (QDRRJAA) is proposed to solve the formulation. In the QDRRJAA, at the beginning of each time slot, the system will exploit the current queue states of all users and channel state information first to calculate four resource scheduling priority matrixes. Then, the category of each resource block (RB), i.e., either an access-only RB, a backhaul-only RB or a common RB can be determined. The theoretical analysis and simulation results reveal that the proposed algorithm can flexibly strike a balance between spectral efficiency and delay by simply adjusting an introduced control parameter and also reduce the required resources for packet transmission. In particular, no iteration and optimization tools are required in the QDRRJAA, which paves the way for practical applications.

The explosive advancements in mobile Internet and Internet of Things challenge the network capacity and architecture. The ossification of wireless networks hinders the further evolution toward the fifth generation of mobile communication systems. Ultra-dense small cell networks are considered as a feasible way to meet high-capacity demands. Meanwhile, ultra-dense small cell network virtualization also exploits an insightful perspective for the evolution because of its superiority, such as diversity, flexibility, low cost, and scalability. In this paper, we specify the necessity of resource management in virtualized ultra-dense small cell networks through a mapping and management architecture and consider the problem of user-oriented virtual resource management. Then, we model the virtual resource management problem as a hierarchical game and obtain the closed-form solutions for spectrum, power, and price, respectively. Furthermore, we propose a customer-first (CF) algorithm that characterizes the user-oriented service of virtualization, and analyze its convergence. Simulation results present the effectiveness of the proposed CF algorithm.

When a full duplex (FD) system is used in a multi-cell environment, the simultaneous transmission and reception cause an increase in inter-cell interference compared with half duplex (HD) systems. This problem is more severe in ultra-dense small cell network (UDN) environments where small cells are extremely dense. Therefore, the performance gain of FD transmission over traditional HD transmission is significantly degraded by this increase in inter-cell interference. To overcome the performance loss, we consider a new FD operation that can avoid the increased inter-cell interference caused by FD use. The basic idea involves separating the small cell base stations into two groups and using half of the communication resources in a manner similar to HD systems. We compare the performance of conventional HD and FD with the proposed scheme in terms of network throughput. Network throughput physically refers to the total throughput per unit area of the network. The simulation results consider indoor and outdoor scenarios and show that our proposed scheme outperforms conventional FD and conventional HD.

A spectrum auction is a promising approach with respect to efficiently allocating spectrum among unlicensed users. In this paper, we study the spectrum auction based on the waveform and air-interface of wireless users, the interests of the channel for the auction, and the interference they suffered during communication as well as their economic capability. How to make the analysis and the integration of such multiple factors is a key problem for multi-slot spectrum auction. To address the problem, we adopt the deep feedforward network algorithm to perform waveform and air-interface data analysis and integration for multi-slot spectrum auction. Simulation results are presented to verify the effectiveness of the proposed algorithm in the small cell network. Our approach could be used to 5G where heterogeneous wireless networks will be applied extensively and spectrum auction decision is made based on deep learning and different user patterns.

In this paper, a renewable energy-based resource allocation method is proposed for full-duplex small cell networks. This method presents an outage-aware power allocation scheme as an optimal transmission policy for a two-way communication system between a base station and user equipment (UE) in one single small cell network, where each node is solar-powered and equipped with a finite capacity battery. A full-duplex (FD) scheduling scheme is presented by utilizing Markov decision process (MDP) action parameters to maximize throughput and minimize outage probability. With two different techniques, scheduling and power transmission, the MDP action sets are formulated to maximize the reward function. Outage probability is investigated by comparing our proposed FD scheme to the conventional half-duplex scheme. Furthermore, the throughput performance of the FD scheme is investigated by varying the scenarios. Finally, the outage probability and throughput performance of the FD scheme are demonstrated, in which the FD scheduling scheme has achieved 87.5 % capacity, and the outage probability is linearly decreased and degraded at 15 dB of SNR. However, with the constraint of a five-meter distance between the UE and interferers, the simulation results show that our proposed optimal transmission policy outperforms the half-duplex system despite dealing with intra-cell interference, inter-cell interference, and limited battery power.

Mobile edge computing (MEC) technology migrates the cloud computing platform to the edge of the mobile access network, which reduces the end-to-end delay of mobile service delivery and improves the user experience. However, since the number of the deployed small cell base stations (BSs) is larger than the macro BSs, automatic deployment can be used to control deployment costs. In this paper, we study the adaptive boundary algorithm of indoor small cell BSs and propose a power optimization scheme in self-organizing MEC-based heterogeneous small cell networks. According to the actual environment, the indoor small cell BS can be deployed quickly and flexibly. And the coverage of the small cell BS can be optimized by automatically adjusting the power parameters. Considering the coverage and spectral efficiency of the macro BS and the small cell BS, the performance indicators of the optimization program and results evaluation is proposed. Finally, the simulation results show that the proposed algorithm and scheme can control the deployment cost and improve the optimization coverage of macro BSs and small cell BSs under the control cost.

In this paper, we study the potential of non-orthogonal multiple access (NOMA) for the purpose of interference mitigation in downlink small cell networks (SCNs). Different from prior works, we focus on opportunistically multiplexing different users on the same subchannel to avoid the severe inter-cell interference brought in by ultradense networking. Aiming to maximize the network throughput, we formulate a distributed subchannel assignment problem with local information exchange. This problem is analyzed through a locally cooperative game model, and the existence of Nash equilibrium (NE) is confirmed by proving that the formulated game is an exact potential game. To solve the problem, we design two concurrent distributed algorithms based on best response (BR) and spatial adaptive play (SAP), respectively. The BR-based algorithm guarantees rapid convergence to an NE, which may not be globally optimal. On the contrary, the SAP-based algorithm can find the global optimum with an arbitrary large probability, although the learning process requires more iterations to converge. Simulation results reveal that the aggregate interference can be more efficiently suppressed in NOMA enhanced networks, which can lead to higher network throughputs. Besides, the superiority of NOMA over orthogonal multiple access is more obvious when the network grows denser.

Recently, due to the increasing popularity of enjoying various multimedia services on mobile devices (e.g., smartphones, ipads, and electronic tablets), the generated mobile data traffic has been explosively growing and has become a serve burden on mobile network operators. To address such a serious challenge in mobile networks, an effective approach is to manage data traffic by using complementary technologies (e.g., small cell network, WiFi network, and so on) to achieve mobile data offloading. In this paper, we discuss the recent advances in the techniques of mobile data offloading. Particularly, based on the initiator diversity of data offloading, we classify the existing mobile data offloading technologies into four categories, i.e., data offloading through small cell networks, data offloading through WiFi networks, data offloading through opportunistic mobile networks, and data offloading through heterogeneous networks. Besides, we show a detailed taxonomy of the related mobile data offloading technologies by discussing the pros and cons for various offloading technologies for different problems in mobile networks. Finally, we outline some opening research issues and challenges, which can provide guidelines for future research work.

Existing spectrum auction algorithms rarely consider the collusion-resistant, which decreases the spectrum allocation efficiency. In this paper, we propose a collusion-resistant spectrum auction algorithm based on K-Nearest Neighbor (KNN) learning in small cell networks. The algorithm can satisfy the increasing requirements of broadband services, improve the utilization of spectrum and also enhance the power-transmitting efficiency in small cells. Considering the interference among small cells, the KNN algorithm is used to classify all the small cells according to the small cells’ geographic locations and interference radius, which can improve the collusion-resistance ability of spectrum auction and improve the spectrum allocation efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithm.

In this paper, we consider a densely deployed phantom cell system providing a low latency network for massive connectivity. Non-orthogonal pilot designs serve as a promising solution to support a large number of users, but fast collision detection at the receiver is needed for low latency networks. Recently, a nice solution based on an on–off type non-orthogonal pilot design with collision detection capability has been proposed. It can serve more users than the orthogonal pilot design but at the cost of degraded channel estimation performance compared to the orthogonal optimum pilot design. We propose a new non-orthogonal pilot design with collision detection capability and improved channel estimation performance. An optimum threshold-based detection criterion is developed. We further show that a dynamically calculated optimum threshold-based detection outperforms a fixed threshold-based detection. Next, we investigate non-orthogonal pilot designs for fractional bandwidth allocation. We propose two new non-orthogonal pilot designs for physical resource block based resource allocation. Both of the new designs support fast collision detection at the receiver. Performance evaluation results show that the proposed schemes provide equivalent or better channel estimation performance and support much more users than the orthogonal pilots defined in the current 4G standards. Finally, we explore sparse channel estimation with compressed sensing technique. We prove several propositions regarding compressed sensing based estimation performance. Using these propositions, two novel orthogonal pilot designs are developed for sparse channel estimation with optimized performances. Finally, utilizing these orthogonal pilot sets, we propose a non-orthogonal pilot design with collision detection capability for sparse channel estimation.

The emerging ultra-dense small cell networks (UD-SCNs) will need to combat a variety of challenges. On the one hand, massive number of devices sharing the limited wireless resources renders centralized control mechanisms infeasible due to the excessive cost of information acquisition and computation. On the other hand, to reduce the energy consumption from fixed power grid and/or battery, network entities (e.g., small cell base stations and user devices) may need to rely on the energy harvested from the ambient environment (e.g., from environmental sources). However, opportunistic energy harvesting introduces uncertainty in the network operation. In this paper, we study the distributed user association problem for energy harvesting UD-SCNs. After reviewing the state-of-the-art research, we outline the major challenges that arise in the presence of energy harvesting due to the uncertainty (e.g., limited knowledge on energy harvesting process or channel profile) as well as limited computational capacities. Finally, we propose an approach based on the mean-field multi-armed bandit games to solve the uplink user association problem for energy harvesting devices in a UD-SCN in the presence of uncertainty.

Ultra-dense deployment of small cell networks is widely regarded as a key role to meet the increasing demand for huge capacity of wireless communication systems. Meanwhile, network densification will result in severe inter-cell interference and thus impair system performance. Traditional radio resource management schemes pay attention to interference mitigation through resource allocation in time, frequency, space, and power domains, or a mix of them. In this paper, polarization, an important and underutilized property of electromagnetic waves, is exploited as a novel means to enhance system capacity. We propose a multicell joint polarization, power and subcarrier allocation (MC-JPPSA) scheme to maximize system capacity through joint optimization of transmitting polarization states, power, and subcarriers with an iterative approach. Though the iterative solution is suboptimal, simulation results demonstrate that MC-JPPSA scheme can strike a balance between performance and complexity compared with the optimal exhaustive search. Furthermore, the proposed scheme outperforms traditional joint power and subcarrier allocation schemes by means of exploiting polarization in ultra-dense small cell networks.

Caching on the edge has been recognized as an effective solution to tackle the backhaul constraint of network densification. However, most related works ignored user mobility in wireless networks, which is unreasonable under the background of network densification. For a more flexible and context-aware caching decision, the concept of caching on the edge can be extended to mobile edge computing (MEC) that enables computation and storage resources at mobile edge networks. With MEC servers deployed on base stations, a huge amount of collected radio access network context data can be analyzed and utilized to render a caching scheme adaptive to user’s context-aware information. In this regard, a novel mobility-aware coded probabilistic caching scheme is proposed for MEC-enabled small cell networks (SCNs). Different from previous mobility-aware caching schemes, user mobility and distributed storage are incorporated into a conventional probabilistic caching scheme, with the aim of throughput maximization. Based on stochastic geometry theory and a modified mobility model of discrete random jumps, the explicit expression of throughput is derived. Due to the complexity of the expression, two light-weight heuristic algorithms are provided to numerically obtain the optimal solutions. Moreover, a significant trade-off among the gains of mobility diversity, content diversity, and channel selection diversity is discussed, and we further numerically analyze how such a trade-off is influenced by user mobility, content popularity, and backhaul capacity, with some fundamental insights into the application of the proposed scheme in MEC-enabled SCNs. The superiority of our proposed scheme is demonstrated by the comparisons with the classical M most popular caching scheme and the conventional probabilistic caching scheme. Numerical results show that our proposed caching scheme achieves higher throughput than those of the other two, especially when users of intense mobility request contents, of which the popularity profile is not skewed, in MEC-enabled SCNs with poor backhaul capacity, indicating that the proposed caching scheme is a promising solution for network densification.

Although the promising 5G cell network technology has increased the transmitting rate greatly, it has also brought some challenges. The energy efficiency has become an important topic in 5G networks. In this paper, the energy efficiency of small cell networks is analyzed, and the existing objective functions are classified in order to minimize the energy consumption, and to maximize the energy efficiency, harvested energy, and energy-aware transmission. Commonly used metrics were analyzed on equipment, base station, and network levels, respectively. Moreover, the methods for energy efficiency improvement were introduced according to above-mentioned metrics. Afterward, the relationships between energy efficiency, spectrum efficiency, and space efficiency were discussed. In order to improve efficiency on equipment, base station, and network levels, the energy and spectrum market is proposed and guidelines for the future research on metrics, methods, and market are presented. The proposed market was verified by simulations, and the simulation results have shown that the proposed market improves the energy efficiencies effectively.