Search

Flying platforms such as Unmanned Aerial Vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial Base Stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of Self Interference Cancellation (SIC) techniques in Full-Duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate, b) maximizing the UL sum-rate, and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one.

Mobile edge computing (MEC) enables computing services at the network edge closer to mobile users (MUs) to reduce network transmission latency and energy consumption. Deploying edge computing servers in small base stations (SBSs), operators make profit by offering MUs with computing services, while MUs purchase services to solve their own computation tasks quickly and energy-efficiently. In this context, it is of particular importance to optimize computing resource allocation and computing service pricing in each SBS, subject to its limited computing and communication resources. To address this issue, we formulate an optimization problem of computing resource management and trading in small-cell networks and tackle this problem using a two-tier matching. Specifically, the first tier targets at the association algorithm between MUs and SBSs to achieve maximum social welfare, and the second tier focuses on the collaboration algorithm among SBSs to make efficient usage of limited computing resources. We further show that the two proposed algorithms contribute to stable matchings and achieve weak Pareto optimality. In particular, we verify that the first algorithm arrives at a competitive equilibrium. Simulation results demonstrate that our proposed algorithms can achieve a better network social welfare than baseline algorithms while retaining a close-optimal performance.

Small cells are regarded as a promising solution for improving the coverage and spectral efficiency of the fifth generation (5G) cellular networks. Small cell networks utilize low transmission power levels and thus, are appropriate for deploying full-duplex (FD) technology. In this paper, a hybrid full/half duplex (FD/HD) small cell network exploiting fractional frequency reuse (FFR) and fractional power control (FPC) for interference mitigation is considered. Theoretical expressions for the coverage probability and the average sum-rate in uplink and downlink directions are derived for systems that make use of conventional frequency reuse, hard FFR, and soft FFR. The impact of various system parameters, such as power control parameter, small base station (SBS) density, and self-interference cancellation (SIC) parameters on system performance are investigated. The numerical experiments show that SBSs tend to operate in FD mode for large values of SBS density and SIC level. Also, interestingly, the optimal sum-rate is achieved when all SBSs operate either in FD or in HD mode. Finally, it is observed that exploiting FFR techniques in FD networks making use of FPC, significantly improves downlink coverage probability.

Fifth-generation networks are becoming a reality, and radio access network architectures are being redefined and rebuilt to accommodate the emerging specifications and demands. The future radio access networks need to incorporate in their architectural design smooth reconfigurability and high flexibility without compromising the throughput and the perceived quality of the new services and traffic types that future networks introduce. Such densely distributed access networks suffer the effect of inter-cell interference. Robust and intelligent coordination of multiple access networks is required in order to minimize the effect of interference and maximize users' gains. Coordinated multipoint operation is already considered as a promising technique for satisfying the challenges of the spectrum and interference management, especially for users at the edge of the cell coverage area. To increase the benefits of coordination multipoint technology in optimizing service quality in dense small cell networks, this study proposes the adaptation of game theory, which will optimize the formation of cooperated base station clusters. The system-level simulations performed indicate that there is a significant increase in the throughput of users located at the edge of cells, without compromising the quality of service experienced by the rest of the users, thus rendering it a suitable candidate for the emerging next-generation networks.

It has been well-recognized that clustered Interference alignment (IA) can provide remarkable interference suppression performance for the existing small cell networks (SCNs). There is also a tendency that full-duplex (FD) radios would replace the half-duplex radios at future small base stations (SBSs). In this context, the intra-cell and inter-cell interference in SCNs would become much more serious, where the performance of clustered IA has not been evaluated yet. In this paper, we explore the maximum achievable sum-rate of the FD-based SCNs when clustered IA combined with power control strategy is applied. To achieve this, a mixed-integer optimization problem is formulated, which is furtherly decoupled into two subproblems for ease of handling. Then we propose the minimized rate loss (MRL) algorithm to address the clustering subproblem and a convex approximation method to address the power control subproblem. The two subproblems are performed alternatively till the sum-rate gains convergence. Preliminary simulations clearly demonstrate that the achievable sum-rate is limited by the number of antennas at the users.

In recent years, wireless communication has experienced a massive shift from a single service (i.e., voice) to an interconnected web of networks. Although many techniques have been developed improving the offered services to mobile users, still the demand for high-quality services cannot be reached. Therefore, this paper proposes a joint non-orthogonal multiple access (NOMA)-enabled optimization framework for small-cell network (SCNet) by utilizing the concepts of multi-objective problem. In particular, the transmit power of base station (BS) in each small-cell simultaneously optimizes to maximize the sum-capacity and total energy efficiency (EE) of SCNet. The multi-objective optimization problem is formulated as non-convex subject to several practical constraints, i.e., individual quality of service requirement, maximum power budget of small-cell BS, and efficient decoding of superimposed signal using successive interference cancellation. Based on the nature of the problem, the optimal solutions are provided using sequential quadratic programming, and Karush-Kuhn-Tucker approaches. The obtained results show significant performance gains over conventional orthogonal multiple access technique in terms of sum-capacity and total EE.

The energy-efficient deployment of small cells helps to reduce environmental pollution in an ultra-dense network. In contrast, demand for massive connectivity and higher data rate are the promise of the present cellular system and small cell networks. Hence, energy consumption is reduced if base stations are optimally used. One way to improve the energy efficiency is by shutting down the redundant BSs while sustaining the Quality of Service for each user. This paper proposes an efficient cell modeling (ECM) algorithm for small cell formation, and binary particle swarm optimization-based small cell deployment (BPSD) to optimize the deployment of small base stations in the small cell network. The small base stations (s-BSs) exist in two modes: active and sleep which is decided by the proposed algorithm without compromising the network performance. The proposed ECM and BPSD algorithms are implemented and evaluated in MATLAB. The results demonstrate that the proposed approaches improve the energy efficiency and connectivity in the ultra-dense small cell network.

Capable of supporting the on-demand high-rate connection towards the core network, wireless millimeter wave (mmWave) backhaul becomes an appealing solution with the growing deployment of small cell base stations (BSs). However, the application of wireless backhaul would impose great limitations on the admission of BSs and the access rate, resulting in a tight coupling between backhaul and radio access network (RAN). In this light, we comprehensively investigate the performance of small cell networks (SCN) in terms of network spatial throughput (ST), supposing that backhaul is conveyed from gateways to BSs through mmWave links. Our results show that the coupling effect makes network ST in the RAN greatly dependent on gateway density $\lambda _{\mathrm{G}}$ and suffer exponential degradation with over-provision of backhaul. It manifests that, if excessive backhaul capacity is provided by deploying more gateways, the exacerbating BS-generated interference deteriorates spatial reuse gain, thereby degrading network ST. Thus, leveraging the coupling between backhaul and RAN, we further conduct a joint optimization of BS and gateway densities to improve network ST. Remarkably, the optimization could mitigate the dependence of network ST on $\lambda _{\mathrm{G}}$ and brings a 2-fold increase in network ST, which is inversely proportional to mmWave beamforming beamwidth.

The neighborhood small cell (NSC) network, which employs indoor small base stations (SBSs) to serve both indoor and outdoor users, is considered a cost-effective solution to address the increasing wireless traffic demand. In this letter, a novel evaluation scheme is proposed to predict the performance of a pure NSC network. A network model composed of indoor ultra-dense small cells (UDS) with coordinated multi-point (CoMP) transmissions is employed to address the maximum achievable performance gain in a given neighborhood. A closed-form analytical model is proposed to approximately compute the signal strength received by a specific outdoor user from the SBSs in a complex-shaped building and verified by the Monte Carlo simulations. The performance prediction of the ultra-dense NSC network is demonstrated in two practical urban scenarios. The proposed evaluation scheme facilitates the tractability of NSC network performance predictions in practical scenarios, which can be employed in future NSC embedded neighborhoods design.

Content caching has limitations on achieving cache gains (e.g., cache hit ratio) in small cell networks, due to limited storages of small base stations (SBSs) and inherent user demand patterns (i.e., initial content preferences). Two effective approaches have been proposed to exploit the potential of content caching: SBS cooperation to utilize cache storage, and proactive content recommendation to shape user demand. In this paper, we investigate cooperative content caching and recommendation to maximize cache gains, while guaranteeing users’ satisfaction by recommending appealing content items. We propose a generic framework for cooperative content caching and recommendation, based on which we propose an online and distributed scheme by designing a continuous-time Markov chain (CTMC). In particular, online content caching (a.k.a., content replacement) is implemented by hopping from one cache state to another in the CTMC, while content recommendation is performed heuristically through sequential fixing at each cache state. Besides, we characterize the performance gap between our proposed scheme and the theoretical optimum in terms of cache hit ratio. Simulation results demonstrate that the proposed scheme achieves better cache hit ratios than other schemes in single-BS scenarios, and provides a competitive solution in multiple-BS scenarios.

Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading for MEC systems has been extensively studied in the literature, service placement is an equally, if not more, important design topic of MEC, yet receives much less attention. Service placement refers to configuring the service platform and storing the related libraries/databases at the edge server, e.g., MEC-enabled Base Station (BS), which enables corresponding computation tasks to be executed. Due to the limited computing resource, the edge server can host only a small number of services and hence which services to host has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service placement in MEC-enabled dense small cell networks. An efficient decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where a network of small cell BSs optimize service placement decisions collaboratively to address a number of challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small cell network. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSP is further extended to work with selfish BSs, where BSs are allowed to choose “to cooperate” or “not to cooperate.” We employ coalitional game to investigate the strategic behaviors of selfish BSs and design a coalition formation scheme to form stable BS coalitions using merge-and-split rules. Simulations results show that CSP can effectively reduce edge system operational cost for both cooperative and selfish BSs.

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.

Wireless content caching in cellular networks is an efficient way to reduce the service delay and alleviate backhaul pressure. For the benefits of sharing spectral and storage resources, clustering in cached networks has recently attracted significant research interests. Meanwhile, since the multimedia content (e.g., video) of caching networks may require a huge transmission rates, millimeter wave (mmWave) communication is considered to be an efficient transmission scheme for cache-enabled networks. We investigate the ergodic rate and average service delay for typical user terminal (UT) in the clustered cache-enabled small cell networks (SCN) and ultra dense networks (UDN) with mmWave channels. In SCN, each cluster consists of cache-enabled UTs, and in the UDN a cluster is formed by cache-enabled UTs and small base stations (SBSs) with non-uniform caching capacity. The clusters are assumed to be discs and content sharing is only possible within clusters through mmWave device-to-device (D2D) tier and SBS tier communications. With stochastic geometry methods, the distributions of content sharing distance and signal-to-interference-noise-ratio (SINR) of typical UT in a cluster are derived for both SCN and UDN scenarios. To minimize the average service delay in high SINR region, we provide an algorithm to jointly optimize caching scheme for SBSs and UTs. By simulations, we validate our theoretical analysis and the performance of proposed caching scheme. The numerical results also show that there exists best radius in the design of cluster for UDNs.

Due to the continuous evolving of mobile communication technologies, deploying small cells (SCs), also known as SC base stations (SBSs), is regarded as one of the feasible alternatives to provide better indoor signal quality to meet the ever-increasing demand for mobile broadband data in indoor environments. However, the co-tier uplink interference, i.e., interference between SBSs, increases when SBSs are densely deployed. Consequently, the sum-capacity of the small cell network (SCN) is deteriorated. To manage such interference, a co-tier uplink power control scheme which combines the Stackelberg game with two-way pricing mechanism is proposed. In this scheme, among the SBSs, one is selected as the leader and the rest are the followers. Under the premise of not violating the maximum tolerable uplink co-tier interference constraint, leader UE and follower UEs achieve the compromised uplink transmit power based on the proposed two-way bargaining procedure. The average sum-capacity of the SCN and the average uplink transmit power of leader UE and follower UEs are used as the major metrics to evaluate the performance of the proposed scheme. Compared with the simulation results obtained by only controlling the transmit power of follower UEs, i.e., Stackelberg game with one-way pricing, the proposed co-tier uplink power control scheme, i.e., Stackelberg game with two-way pricing, attains higher sum-capacity with smaller transmit power.

The combination of energy harvesting small cell networks (EH-SCNs) and mobile edge computing (MEC) has been considered as an effective means to improve the performance of mobile networks and provide users with a higher quality of service (QoS). In this letter, we investigate the decentralized computation offloading problem in heterogeneous EH-SCNs with MEC, where heterogeneous small cell base stations (SBSs) are rational individuals with interests to maximize their own benefits while considering their QoS requirements. Different from existing works, we address the challenge that heterogeneous SBSs may unwilling to expose their own information about the system state and offloading decisions. We formulate the problem as a partially observable stochastic game (POSG), in which SBSs can make optimal offloading decisions with imperfect state information. We analyze the local equilibrium, and propose a stochastic offloading algorithm to obtain the approximate optimal solution. Numerical results validate the effectiveness of the proposed scheme.

In Cache-enabled dense small cell networks (DSCNs), energy consumption and file delivery delay are two critical performance metrics. On the other hand, power control is an essential mechanism to mitigate inter-cell interference in DSCNs. However, the impact of power control at small base stations (SBSs) on the system performance when caches are involved has not been well studied. Observing energy consumption and file delivery delay are coupled with each other in cache-enabled DSCNs, different from existing studies, we attempt to optimize these two performance metrics at the same time, taking into account sophisticated power control at SBSs. Firstly, we formulate the energy-delay optimization problem as a utility maximization problem, where cooperative power control among SBSs, file placement and user association are considered. Then, we solve the optimization problem in two stages (i.e., caching stage and delivery stage), based on the fact that caching is performed during off-peak time. At the caching stage, a local popular file placement policy is proposed by estimating user preference at each SBS. At the delivery stage, with given caching status at SBSs, the optimization problem is further decomposed by Benders’ decomposition method. An efficient algorithm is proposed to approach the optimal association and power solution by iteratively shrinking the gap of the upper and lower bounds. Finally, extensive simulations are performed to validate our analytical and algorithmic work. The results demonstrate that the proposed algorithms can achieve a better tradeoff between energy consumption and file delivery delay than the existing algorithms.

In licensed assisted access (LAA) based small cell networks (SCNs), the small base station (SBS) can reuse the uplink licensed bands with the macro cell while sharing the unlicensed bands with the Wi-Fi networks to improve its throughput. To mitigate the severe co-channel interference to the macro cell and guarantee the harmonious coexistence with the Wi-Fi networks, the spectrum and power should be jointly allocated at the SBSs. Moreover, to overcome the overwhelming signaling overheads introduced by the traditional centralized scheme and adapt to the variable radio environments, an adaptive decentralized scheme is necessary. Therefore, in this paper, an adaptive semi-distributed scheme is proposed to jointly allocate the power and spectrum on both licensed and unlicensed bands, which can achieve the global optimal spectrum efficiency (SE) of the SCNs. The proposed scheme can enable the SBSs to work independently and adaptively without sharing the whole information of the SBSs, but requires some Lagrangian parameters exchange via the coordination of the macro base station (MBS). Theoretical analysis and numerical results are presented to show that the proposed scheme is capable of achieving the optimal SE on both licensed and unlicensed bands adaptively while confining the co-channel interference to the MBS and guaranteeing the fair coexistence with the Wi-Fi network.

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) enabled heterogeneous small cells network (HSCN), where the macro base station simultaneously communicates with multiple small cell base stations (SBSs) through wireless backhaul. In each small cell, users are grouped by NOMA bases and then served by their respective SBS. The proposed framework considers the realistic imperfect channel state information and quality of service requirements of users. The goal is to investigate an energy-efficient joint power, and bandwidth allocation scheme, which aims to maximize the energy efficiency (EE) of the small cells in downlink NOMA-HSCN constrained by the maximum transmit power and the minimum required data rate simultaneously. The optimization problem is non-convex due to the fractional objective function and non-convex constraint and thus challenging to obtain an exact solution efficiently. To this end, the joint optimization is first decomposed into two subproblems. Then, an iterative algorithm to solve the power optimization subproblem is proposed with guaranteed convergence. Furthermore, we derive a closed-form solution for the bandwidth allocation subproblem. Simulation results reveal that the effectiveness of the proposed schemes in terms of EE compared to the existing NOMA and the orthogonal multiple access schemes.

In this paper, a power saving scheme is investigated for macrocell-assisted small cell networks in the downlink, in which small cells are selected to stay in sleep or wake-up modes in order to mitigate excessive power consumption and the small cells operating in the sleep mode are eligible to off-load their served users to the macrocell to preserve communications. By taking the dynamics of user arrival into account, we attempt to minimize the average total power consumption of the network, which depends on several network parameters like power consumption of base stations, available bandwidth, user load in the cells, cell size, user rate requirement, rate outage probability and noise power density, while ensuring the average number of dropping off-loaded users to a satisfactory level. The design problem is then formulated as a constrained Markov decision process and solved via linear programming. A randomized strategy is proposed to accomplish the optimal sleep/wake-up policy for small cells. Simulation results confirm the effectiveness of the proposed scheme, as compared with the one without adopting sleep/wake-up mechanisms, and help us to capture the impact of the network parameters on the entire power saving.

In this paper, a cross-tier cooperation framework in H-CRAN is proposed to mitigate inter-tier interference. Specifically, a small cell remote radio head (S-RRH) acts as the relay for multiple macrocell users (MUEs), and obtains a fraction of time slot from multiple MUEs as a reward. Through the cooperation, the S-RRHs can obtain extra spectrum resource for serving small cell users (SUEs) which are secondary users, while the MUEs which are primary users can improve their data transmission utility. The cooperation problem is formulated as a mixed integer programming, which is NP-hard. To solve this problem, we transform the cooperation problem into a joint cooperation time and power allocation problem and a cooperator selection problem. First, we solve the joint cooperation time and power allocation problem to obtain the maximum cooperation utility of all the possible cooperating pairs. We derive the closed-form solution for the allocation problem by using linear independence constraint qualification and Karush-Kuhn-Tucker conditions. Then, we solve the cooperator selection problem by a two-sided matching algorithm. Extensive simulation results show that the utility of macrocell networks and small cell networks can be improved by adopting the proposed cooperation framework, and the cooperator selection result is stable and close-to-optimal.

The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.

Long-term evolution in unlicensed spectrum (LTE-U) can make use of centralized scheduling, interference coordination and other technologies to achieve better spectrum efficiency (SE). As the evolution of cloud computing, mobile edge computing (MEC) sinks the computing and storage capacity from the centralized data center to the edge of the network, which is the key technology to achieve low delay and high speed. However, the traditional centralized scheme in small cell networks (SCNs) structure will bring huge signal overheads. In order to adapt to the complex and changeable network system, this paper proposes a distributed resource and power allocation scheme under MEC environment, which enables small base stations (SBSs) to work autonomously. The SBSs need only little information exchange through the information cloud (IC) and finally obtain the global optimal SE. Simulation results confirm the correctness and effectiveness of the proposed scheme, and demonstrate the superiority of the distributed scheme over the centralized scheme.

We study the performance of a cognitive small cell network, catering multicast services to multiple groups of secondary users, using a pre-assigned set of orthogonal channels of primary users present in the corresponding macrocell. We consider the hybrid mode of cognitive radio operation for efficient spectrum access, where cooperative spectrum sensing is performed by the collocated secondary users of each group to ensure better protection to primary users’ communication. Considering delay-sensitive application for each multicast group and availability of a rate adaptive application layer and a power adaptive link layer, we aim to maximize the energy efficiency of the small cell base station using a cross-layer approach. More precisely, a joint optimization problem involving sensing time, rate and power allocation is formulated to maximize the energy efficiency of the small cell network under the probabilistic interference constraint of each primary user and heterogeneous quality of service constraints of each SU multicast group. The problem is found to be generally non-convex and an iterative algorithm is proposed to find either an optimal or a near-optimal solution. Simulation results are presented to analyze the performance of our proposed scheme in terms of energy efficiency concerning some key system parameters. The results confirm that our solution is much more energy-efficient than the conventional approach with only transmit power adaptation.

Due to the fact that the higher density of mobile devices is expected, the fifth generation (5G) mobile networks introduce small cell networks (SCNs) to prevent exhausting radio resources. SCNs improve radio spectrum utilization by deploying more base stations (BSs) in the networks. Even though authenticated key exchange (AKE) is still essential to ensure entity authentication and confidentiality in mobile communications. Besides, user anonymity is required to guarantee the footprints of mobile communications being concealed. However, AKE with user anonymity may increase the latency of communications dramatically in total due to several times more frequency in SCNs. The increase of latency will be more serious when an AKE protocol supports user anonymity, where traceability and revocability to users are necessary. Thus, this paper presents a secure region-based handover scheme (ReHand) with user anonymity and fast revocation for SCNs. ReHand greatly reduces the communication latency when user equipments (UEs) roam between small cells within the region of a macro BS, i.e., eNB, and the computation costs due to the employment of symmetry-based cryptographic operations. Compared to the three related prior arts, ReHand dramatically reduces the latency from 82.92% to 99.99% by region-based secure handover. Nevertheless, this paper demonstrates the security of ReHand by theoretically formal proofs.

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.

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.

This paper presents a tactic to realize numerous in-building small cell base station (SBS) architectures for dynamic spectrum sharing by varying the number of physical transceivers as well as the number, amount, and characteristics of operating spectrums per SBS. Each SBS architecture is defined as a Type and the spectrum sharing mechanism of each Type of architecture is detailed and mathematically analyzed to define existing dynamic spectrum sharing techniques suggested in the literature. The high external wall penetration loss of a building and the eICIC technique are exploited to manage co-channel interference generated due to sharing the spectrum of one system to another. System-level capacity, spectral efficiency, and energy efficiency performance metrics are derived for each SBS architecture to show the relative outperformance of one to another. It is found that, unlike energy efficiency (EE), the spectral efficiency (SE) response of an SBS architecture is directly affected by the channel characteristics as well as the number and amount of operating spectrum bands. However, the number of transceivers of an SBS does not have a noticeable impact on both SE and EE so long as the feature of an operating spectrum is not altered. Finally, we show that each Type of the proposed SBS architectures can achieve the prospective SE and EE requirements for fifth-generation (5G) mobile networks.

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.

In traditional cache-enabled small-cell networks (SCNs), a user can suffer strong interference due to contentcentric base station association. This may degenerate the advantage of collaborative content caching among multiple small base stations (SBSs), including probabilistic caching and coded caching. In this work, we tackle this issue by deploying multiple antennas at each SBS for interference management. Two types of beamforming are considered. One is matched-filter (MF) to strengthen the effective channel gain of the desired signal, and the other is zero-forcing (ZF) to cancel interference within a selected SBS cooperation group. We apply these two beamforming techniques in both probabilistic caching and coded caching, and conduct performance analysis using stochastic geometry. We obtain exact and approximate compact integral expressions of system performances measured by average fractional offloaded traffic (AFOT) and average ergodic spectral efficiency (AESE). Based on these expressions, we then optimize the caching parameters for AFOT or AESE maximization. For probabilistic caching, optimal caching solutions are obtained. For coded caching, an efficient greedy-based algorithm is proposed. Numerical results show that multiple antennas can boost the advantage of probabilistic caching and coded caching over the traditional most popular caching with the proper use of beamforming., Comment: 15 pages, 10 figures, accepted by Transcation on Wireless Communication

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies.

Demand for mobile data services is increasing exponentially and operators have responded by expanding and upgrading their networks to enhance capacity. One promising technique is to densify the network with low-power and small-coverage base stations (BSs) to provide significant capacity gains. In this paper, we develop a multi-user connectivity model for a Poisson Point Process (PPP)-based homogeneous network to facilitate a realistic study of its energy performance subject to blocking constraints. We then derive the average rate per subchannel and per user and compute the average sum rate of the network. An area power consumption (APC)-minimization framework is then formulated subject to appropriate coverage and average rate constraints to determine the optimal deployment configuration of BS density and associated transmit power. Furthermore, a new sleep mode mechanism called centralized strategic scheme is analyzed to determine its ability to adapt energy consumption to variable user density. Our results show that sleep mode is a viable solution for managing network energy consumption in dense networks.

In small cell networks, content placement can reduce the content download delay at backhaul links while imposing challenges on user association problems. Specifically, a user may be associated with a small base station (SBS) that has the desired contents but is far away, increasing the delay over radio access links. To this end, we investigate the joint content placement and user association problem to reduce the average download delay over backhaul and radio access links for content delivery. Considering both dynamic user population and natural tandem systems, we model content delivery in each SBS as a tandem queue and thus derive the average sojourn time (the average time contents stay in the tandem queue), i.e., the average download delay. On this basis, we formulate a delay minimization problem, encompassing user characteristics (traffic arrival rates, requested content lengths and content preferences) and SBS constraints (backhaul capacity and storage size). Then, this problem is decomposed into a user association subproblem and a content placement subproblem by exploiting the biconvexity. Finally, a distributed content placement and user association scheme is proposed by exploring the optimal match with the first derivative length method. Extensive simulation results reveal that the delay under the proposed content placement policy may be reduced as user characteristics become heterogeneous, which is opposite to the case under user association policies.

Caching at base station is a promising technology to reduce the backhaul loads in future wireless networks. To exploit the content diversity gain and multiple antenna transmission, in this paper, we consider the analysis and optimization of random caching in multi-antenna small-cell networks with limited backhaul. We analyze the performance of the success probability, which is defined as the probability that the requested file is not only obtained from the cache or the backhaul at the small base station (SBS), but also successfully delivered via the wireless link from the SBS to the user. Using tools from stochastic geometry, we first derive an exact expression and an upper bound of the success probability. Based on the analytical results, we then consider network throughput maximization by optimizing the design parameters in the cache-enabled multi-antenna small-cell networks. The optimization problem is a complicated mixed-integer optimization problem. After analyzing the optimal properties, we obtain a local optimal solution with low complexity. To further simplify the optimization, we solve an asymptotic optimization problem in the high user density region, using the upper bound as the objective function. Numerical simulations show that our proposed scheme improves the performance by up to $14\%$ when the Zipf parameter is 0.4 and the antenna number is 8.

Dense deployment of small cell base stations (SBSs) powered by renewable energy is spotlighted as a solution of the increasing demand of communication services. How to utilize renewable energy efficiently under the temporal change of traffic loads is a brand new challenge. In this paper, we design real-time controllers for cell zooming, that is, to determine transmission power and activation/deactivation of energy harvesting SBSs. The proposed method maximizes the total number of accommodated users, considering the remaining energies of batteries. First, we solve a problem of finding a number of active SBSs that maximizes energy efficiency with full accommodation of active users. Then, we apply model predictive control to the real-time computation of an optimal transmission power. Moreover, we present an explicit formula of an approximate optimal transmission power, by simplifying the proposed method. Numerical simulations illustrate that the proposed method achieves high performance on energy efficiency with low computational effort, compared with a Q-learning-based approach.

Heterogeneous Networks (HetNet) requires the deployment of small cell networks that can co-exist with the existing macro-cell. To provide a high Quality of Service (QoS), a massive multiple-input-multiple-output (MIMO) must be equipped with a HetNet of a macro-cell base station to the multiple users. This paper highlights three important aspects of the emerging small cell wireless networks. First, the architectures of small cell networks in LTE wireless network is reviewed, with specific references to the current wireless network standards. Second, the performance evaluation of overlay small cells integrated with the underlay macro-cell is investigated. The third part of this study focuses on the future trends of small cell deployment in 5G and the critical technical preparation needs to be done to allow integration with the existing 4G network. The results show that the proposed integration of 5G small cells into existing macro 4G networks has improved both of the data rate (91.46%) and energy efficiency (98.66%).

In the dense small cell networks, CoMP is an efficient way to improve transmission rate. Unfortunately, CoMP would increase the backhaul resource consumption, which is not trivial for the small cell networks with limited wireless backhaul. On the other hand, the small cell base stations can cache locally some files in advance to alleviate the payload on backhaul. In this paper, we study the effect of distributed caching and the limited backhaul resources on the design of CoMP, and propose a CoMP precoding design method, which can be performed in a distributed way. Our simulation results show that considering both backhaul and caching is important for designing the appropriate CoMP precoding, and our proposed method produces higher throughput than the existing CoMP precoding design scheme.

Unmanned aerial vehicles (UAVs) provide flexible wireless access to future wireless networks and have wider coverage with higher transmission rates. However, because the wireless backhaul for UAVs is usually capacity limited and congested, and, because UAVs cannot operate for long periods of time due to their limited battery life, we propose a framework of caching UAV-enabled smallcell networks to offload the data traffic of small-cell base stations (SBSs). In the proposed scheme, the most common content is stored in local caches of UAVs in advance and delivered to mobile users directly from the caches when required. Thus, the wireless backhaul congestion can be alleviated, the energy consumption can be reduced, and the quality of experience (QoE) can be improved. Based on this framework, typical schemes of caching UAV-enabled small-cell networks are demonstrated, and the distributed caching strategy for UAVs is discussed. For illustration, a case study of interference management is also presented. Finally, several interesting research issues and challenges are discussed for caching UAVenabled networking.

We consider the interference alignment (IA) in small cell networks with full-duplex (FD) self-backhauling equipped on each small base station. First, by leveraging the optimization theory and formulating the IA conditions as a 0–1 integer linear programming (ILP) problem, we establish that the optimal solution of the formulated ILP problem is exactly equivalent to that of its associated relaxed linear programming one, then proposing an efficient approach to judge the IA feasibility, even in FD-based small cell networks with more interference signals. Then, given perfect interference elimination, an adaptive power allocation (PA) problem is formulated, followed by an iterative algorithm leveraging first-order approximations, with its convergence assured. Finally, simulations are conducted to reveal the effectiveness of the proposed algorithms, showing that the sum rate of FD-enabled networks can benefit from not only the introduction of FD self-backhauling but also the execution of adaptive PA.

International audience; In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small cells are combined to offer better Quality of Service to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) joint transmissions are used to further improve performance. In this paper, we address the average delay minimization problem by first formulating it as a static optimization problem. Even though the problem is NP-hard we are able to solve it via an efficient algorithm that guarantees a 1 2-approximation ratio. We then proceed to propose two fully distributed and dynamic caching policies for the same problem. The first one asymptotically converges to the static optimal solution under the Independent Reference Model (IRM). The second one provides better results in practice under real (nonstationary) request processes. Our online policies outperform existing dynamic solutions that are PHY-unaware.

A rapid increase in the wireless internet-based applications led to an enormous increase in wireless data rates. Intensification of future wireless networks faces a great challenge to meet such growing demand for payload data. A suggested solution that can be used to resolve this issue is to overlay small cell networks with macro cell networks to provide higher network capacity and better coverage. Small cell networks experience large interference from macro cell base stations (BSs) making data rates received by the small cell users not reliably. In this paper, an antenna selection scheme based on small cell user’s (SCU) channel gain is proposed. Whereas, the two tiers use the same network bandwidth resources; the macro BS selects a subset of antennas which has a minimum interfering effect to the SCU based on a pilot sent from SCU to macro cell. The proposed selection scheme has been compared with convex optimization antenna selection scheme. Simulation results show that the SCU data rates are significantly improved using proposed scheme. Execution time required for antenna selection is reduced significantly using the proposed scheme.

The physical characteristics of fifth generation (5G) cellular network wavelengths result in quicker attenuation and smaller base station (BS) coverage area, which in turn, cause BSs to naturally transform into small cell BSs (SBSs). However, in the case of fixed-rail transportation, they often cover long distances when deployed and pass by multiple SBSs, hence, the connection of the terminal equipment in these forms of transportation is subjected to frequent handovers. Therefore, it is important to address the issue of identifying suitable SBSs for each handover such that a certain level of service quality and non-interruptible transmission can be achieved. In the present study, we proposed a new handover selection algorithm named the toss-and-catch algorithm. By means of an efficient SBS selection mechanism and configuration settings, the algorithm selects suitable SBSs to ensure reliable transmission and non-interruptible handovers. Meanwhile, with the assistance of an overload support mechanism, the algorithm is able to resist changes in channel environments under most conditions. In order to apply our results in more realistic channel environments, we performed all-inclusive simulations based on different symmetric fading channel environments, with the aim of developing more practical SBS selection and handover methods for mobile terminal equipment. The multitude of simulation results indicates that from the perspective of terminal equipment in fixed-rail transportation, under most conditions, the performance of the toss-and-catch algorithm in terms of signal quality and handover connection was superior to those of other conventional methods. For example, the toss-and-catch algorithm outperformed the random SBS selection method in a typical fading channel environment (e.g., Nakagami-1 fading), achieving, on average, an approximately 28% improvement in signal quality, an approximately 50% reduction in the disconnection rate for handover connections, and an approximately 71% improvement in processable load ratio. These results indicate that the toss-and-catch algorithm allows for a greater number of suitable SBS handover candidates to be identified, making it a promising SBS handover selection mechanism for 5G fixed-rail transportation networks.

Interior wall blockage affects the performance of indoor cellular networks but has not been sufficiently studied. In this paper, by modelling the spatial distribution of interior walls using the random shape theory and incorporating the attenuation caused by wall blockages into the path loss model, we develop a tractable approach to analyse the performance of indoor small cell networks under the influence of interior wall blockages. Then, the proposed analytical approach is applied to study the downlink coverage probability of two user association strategies: the closest line-of-sight (LOS) base station (BS) strategy, and the closest non-line-of-sight (NLOS) BS strategy. The analytical results are validated by Monte Carlo simulations in comparison with those obtained under the existing wall blockage model that assumes impenetrable walls. Our results reveal the importance of considering the wall attenuation for the accurate analysis of indoor cellular networks. The coverage probability is more sensitive to wall attenuation under the closest NLOS BS user association strategy than under the closest LOS BS user association strategy, and that there is an optimal BS density that maximizes the coverage probability under the closest LOS BS user association strategy, but the coverage probability under the closest NLOS BS user association strategy monotonically decreases with the increase of BS density.

In this paper, a stochastic geometry approach is used to model and analyse a downlink small cell network (SCN). Carrier sense multiple access with collision avoidance (CSMA/CA) is used as medium access control (MAC) protocol at the base stations (BSs). To boost spectral efficiency, each active BS deploys both orthogonal multiple access (OMA) and nonorthogonal multiple access (NOMA) to transmit signals to the user equipments (UEs) in its coverage area. The choice between OMA and NOMA mode is based on the density of UEs and aims to improve the sum rate of the BS. If there exist two UEs that sufficiently differ in their channel power gains to the BS, the BS will use NOMA superimposing the signals of the UEs in the power-domain. Otherwise, the BS will operate in OMA mode to transmit the signal of a single UE in its coverage area. Stochastic geometry is used to include the spatial densities of the UEs and BSs in the performance assessment of the SCN. On this basis, analytical expressions for the coverage probability, data rates of the UEs, and sum rate of the BSs of the considered system are derived. Numerical results are provided to illustrate the impact of system parameters on the performance of this SCN with NOMA subject to the spatial densities of the BSs and UEs. open access

Heterogeneous small cell networks have been regarded as a promising approach that can reduce energy consumption in order to increase the overall capacity of the system and meet increasing traffic demand. In a heterogeneous small cell network, by deploying a small cell base station in the service range of the macro BS, the coverage of the existing macro BS can be improved and the capacity of the entire network can be extended. However, cellular network accounts for more than 10% of the total carbon footprint of information and communication technology (ICT) business, and BS accounts for more than 50% in ICT business. Therefore, in this paper, focusing on minimizing power consumption of BS, we provide a survey on BS positioning strategy and BS switching strategy.

This paper considers clustered small cell networks (SCNs) with combined design of cooperative caching and energy-efficient policy in the Coordinated Multi-Point (CoMP)-enabled cellular network. Small base stations (SBSs) with cache storage are grouped into associative clusters which can communicate with each other. This paper focus on movie on-demand streaming from Internet-based servers and proposed combined caching mode, where every SBS utilizes parts of cache space to cache the most popular contents (MPC), while the remaining is used for cooperatively caching different partitions of the less popular contents (LPC). Instead of the known content popularity, we constructs a content-aware weighted feature matrix (CWFM) in terms of spatiotemporal variation. Based on estimated content popularity and transmission design, we propose a caching scheme that makes a caching decision to maximize the energy efficiency (EE). To tackle this problem, A two-step stepwise optimization method is adopted. First, EE conditioning is optimized with a approach of linear programming and variable recovery. Then, the optimal proportion of cache space for MPC is analyzed by comparing the energy-efficient gain from the MPC with the energy-efficient loss from the discarded contents. Extensive simulation results confirm that our algorithm outperforms state-of-the-art algorithms based on MovieLens data set.

International audience; We consider a dense cellular network, in which a limited-size cache is available at every base station (BS). Coordinating content allocation across the different caches can lead to significant performance gains, but is a difficult problem even when full information about the network and the request process is available. In this paper we present q LRU- Δ , a general-purpose online caching policy that can be tailored to optimize different performance metrics also in presence of coordinated multipoint transmission techniques. The policy requires neither direct communication among BSs, nor a priori knowledge of content popularity and, under stationary request processes, has provable performance guarantees.

Recently, non-orthogonal multiple access (NOMA) small cell networks (SCNs) have been studied to meet the stringent requirements for spectral efficiency and massive connectivity in the emerging 5G networks. This paper aims at addressing the overall resource orchestration issue in 5G SCNs, by considering the problem of joint user to small cell association and uplink power allocation, employing NOMA technology. In particular, the power allocation is performed under an incomplete information scenario, where the users’ channel conditions are unknown to the small cell. To treat this issue in an effective manner, contract theory is adopted in order to incentivize each user to select the power level that optimizes its own utility, while each small-cell base station (SBS) rewards them inversely proportionally to their respective sensed interference. The proposed framework is complemented by a distributed user-cell association mechanism based on reinforcement learning (RL). Indicative numerical results are provided to validate the operation and effectiveness of the proposed contract-theoretic approach.

Macro base stations are densely overlaid by small cells to satisfy the demands of user equipment in heterogeneous networks. Due to their dense deployment, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipment to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency. We develop centralized and distributed user association algorithms based on sequentially minimizing the power consumption. We evaluate the performance of the proposed algorithms under two scenarios and show that they achieve higher energy efficiency compared to the existing algorithms in the literature, while maintaining high spectral efficiency and backhaul load balancing.