Small Base Stations Research Articles

Joint uplink/downlink fair power allocation for wireless backhaul of vehicular small cells

This paper proposes a two-part joint uplink/downlink power allocation for the backhaul of the vehicular small cell, one part for the uplink and another part for the downlink. In the first part of the proposed algorithm, after macro base station assignment for each small base station, we allocate the transmission power using the pilot assisted approach in the uplink direction. Simultaneously, using a normalizing factor obtained from the first part of the algorithm, which is representing the SINR balancing level of the small cell layer in the uplink direction, the second part is implemented using the Foschini concept to achieve the SINR balancing in the downlink direction just equal to that in the uplink direction. Therefore, the same levels for the SINR balancing are fairly obtained jointly for the macrocell and small cell layers in the downlink and uplink directions, respectively. Then, we provide some analysis on the convergence of the proposed algorithm in both parts of the uplink and downlink. In contrast to previous works, it is not required to be solved a non-convex optimization problem for the implementation of our proposed algorithm. Simulation results illustrate that the proposed scheme converges to the SINR balancing level after a few iterations in both uplink and downlink directions of the backhaul link.

Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

Performing cellular long term evolution (LTE) communications in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and f ractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as homo egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with wireless local area network and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium, when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network.

Random Caching Based Cooperative Transmission in Heterogeneous Wireless Networks

Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink large-scale HetNet. We propose two SBS cooperative transmission schemes under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry and adopting appropriate integral transformations, we first derive a tractable expression for the successful transmission probability under each scheme. Then, under each scheme, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, under each scheme, we obtain a locally optimal solution in the general case and a globally optimal solution in some special cases. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching under each scheme achieves a promising successful transmission probability. The analysis and optimization results provide valuable design insights for practical HetNets.

A user‐centric cooperative edge caching scheme for minimizing delay in 5G content delivery networks

AbstractThis paper investigates the problem of user‐centric cooperative edge caching in content delivery networks to leverage service provisioning at the network edge and to improve the quality of experience by minimizing the end‐to‐end delay. By taking advantage of the major characteristics in fifth‐generation networks, users can access contents not only from the nearest small base station (SBS) but also from other SBSs in the vicinity that have the requested precached contents. In the proposed scheme, a user‐centric delivery approach is considered in such a way that the base station can respond to the user request as long as it has enough resources. To this end, a caching algorithm is introduced whereby a group of SBSs cooperatively share storage and decide on the caching policy together aiming to cache as much contents as possible under the capacity constraint. Moreover, a modified matching theory is used for content delivery taking the bandwidth requirements into account. Simulation results show that the proposed scheme can achieve 99.5% local serve ratio when the SBS has a caching capacity of 40% the overall file size, and 20 available communication channels for content distribution.

Intelligent Interference Exploitation for Heterogeneous Cellular Networks Against Eavesdropping

This paper explores the co-existence of a macro cell and a small cell for heterogeneous cellular networks, where a macro base station (MBS) and small base station (SBS) transmit to respective macro user (MU) and small user (SU) through their shared spectrum in the face of a common eavesdropper. We consider two spectrum sharing mechanisms, namely the overlay spectrum sharing (OSS) and underlay spectrum sharing (USS). In the OSS, the MBS and the SBS take turns to access their shared spectrum. By contrast, the USS allows the MBS and SBS to simultaneously transmit over the shared spectrum with the aid of power control for limiting their mutual interference, thus called interference-limited USS (IL-USS). In order to take advantage of mutual interference in confusing the eavesdropper without causing adverse effect on the MU, we propose an interference-canceled USS (IC-USS) scheme, where a sophisticatedly designed signal is emitted at the MBS to cancel out the interference received at the MU, which is also beneficial in terms of defending the common eavesdropper. Closed-form expressions of overall outage probability and intercept probability are derived for OSS, IL-USS, and IC-USS schemes by taking into account both MBS-MU and SBS-SU transmissions. The secrecy diversity analysis is also carried out by characterizing an asymptotic behavior of the overall outage probability with a given intercept probability in the high signal-to-noise ratio region. It is shown that the secrecy diversity gains of conventional OSS and IL-USS are zero, whereas the proposed IC-USS achieves a higher secrecy diversity gain of one. This implies that with an arbitrarily low overall intercept probability, the conventional OSS and IL-USS methods converge to their respective outage probability floors; however, the proposed IC-USS scheme can make the overall outage probability asymptotically decrease to zero by simply increasing the transmit power. In addition, numerical results demonstrate an obvious advantage of the proposed IC-USS over OSS and IL-USS against eavesdropping.

Optimal Caching in 5G Networks With Opportunistic Spectrum Access

Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestion at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability in multi-channel cache-enabled 5G networks with both unicast/multicast capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing the opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. An optimization framework for file caching is then developed to maximize the hit probability. To this end, a simple concave approximation for the hit probability is proposed, which highly reduces the optimization complexity and leads to a closed-form solution. The sub-optimal solution is benchmarked against two widely employed caching distribution schemes, namely, uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the caching strategy should be adapted to the network parameters and capabilities. For instance, diversifying file caching according to the Zipf distribution is better in multicast systems with large number of channels. However, when the number of channels is low and/or the network is restricted to unicast transmissions, it is better to confine caching to the most popular files only.

Performance Analysis of Cache-Enabled Millimeter Wave Small Cell Networks

Millimeter wave (mmWave) small-cell networks can provide high regional throughput, but the backhaul requirement has become a performance bottleneck. This paper proposes a hybrid system that combines traditional backhaul-connected small base stations (SBSs) and cache-enabled SBSs to achieve the maximum area spectral efficiency (ASE) while saving backhaul consumption in mmWave small cell networks. We derive and compare the ASE results for both the traditional and hybrid networks, and also show that the optimal content placement to maximize ASE is to cache the most popular contents. Numerical results demonstrate the performance improvement of deploying cache-enabled SBSs. Furthermore, given a total caching capacity, it is revealed that there is a tradeoff between the cache-enabled SBSs density and individual cache size to maximize the ASE.

Secure Transmission With Guaranteed User Satisfaction in Heterogeneous Networks: A Two-Level Stackelberg Game Approach

In this paper, secure transmission in a heterogeneous network in the presence of multiple eavesdroppers is studied. A game-theoretic framework is proposed to enhance the security of the macro base station (MBS), while guaranteeing user satisfaction of the small base stations (SBSs) by exploiting the cooperation and competition among the entire network. Specifically, a two-level Stackelberg game is formulated, where the MBS employs a set of competing jamming SBSs to jam the eavesdroppers at the top level, and each employed jamming SBS may require offloading service from multiple competing helping SBSs in its cluster at the bottom level if needed. Two levels of user satisfaction are investigated at the bottom level, respectively. One is fixed with the priority given to user satisfaction over the profit and the other is flexible with a balance between user satisfaction and the profit. The Stackelberg equilibrium of the proposed game is derived and analyzed from the economics viewpoint. An iterative algorithm is also proposed to obtain the optimal solutions. Numerical results are provided to verify the theoretical analysis and show the effectiveness of the proposed algorithm.

On Power-Efficient Planning in Dynamic Small Cell Networks

We consider a dense small cell network underlaying the conventional cellular infrastructure, where the macro base station (MBS) activates some small base stations (SBSs) in case it is necessary to offload the macro cell traffic. In order to become activated, every SBS is allocated the required power supply by the MBS. We develop a realistic system model where the MBS does not have any prior information on small cells’ characteristics such as local channel qualities or user traffic, and the network’s statistical characteristics are time-variant. We model the energy-efficient SBS activation problem as a budget-limited multi-armed bandit game where the reward generating processes of arms are piece-wise stationary. We develop a decision making policy to solve the energy-efficient activation problem, and prove an upper-bound for the regret. The performance of the proposed scheme is evaluated also through numerical analysis.

Heterogeneous Ultra Dense Networks: Part 2

The eleven articles in this special section focus on heterogeneous ultra dense networks. With the continuous enrichment of mobile communication application scenarios in the future, the traditional macrocellular-based mobile communication network architecture will be challenged to meet the explosive growth in demand for communications services. As described in the Guest Editorial of the first part of this Feature Topic, the ultra-dense network (UDN) has become a promising technology to signifi cantly improve the network spectral efficiency and system performance. Because the massive number of small base stations have the advantages of low transmission power and flexible deployment, they help to effectively expand the coverage and throughput of the network. Hence, in heterogeneous UDN, the dense deployment of small base stations can signifi cantly improve the quality of the links and increase the network capacity. As the key technology of 5G and the advanced communication technology at the present stage, UDN has received worldwide attention and eff orts.

Downlink and Uplink Decoupling in Heterogeneous Networks for 5G and Beyond

To meet the explosively increased data traffic demand from 5G and beyond, one of the most promising technologies is densified heterogeneous networks (HetNets). In HetNets, base stations (BSs) are brought closer and closer to users by densifying small BSs, which results in considerable high spectral efficiency and energy efficiency for cellular systems. However, HetNet topology challenges the traditional cellular systems. One of the most important challenges is user association. Aiming to avoid the drawback brought by coupled access (CA) in user association of HetNets, a decoupled access (DA) approach has emerged recently. By allowing access points in uplink (UL) and downlink (DL) association to be different, DA significantly improves UL performance of HetNets. This article focuses on recent works in DA. First, typical scenarios of HetNets for DA are described, which is followed by presenting two major decoupled association policies under typical scenarios. Then, DA shows its superiority over CA under different scenarios and decoupled association policies via various performance metrics. Finally, future work on DA in HetNets is discussed.

Cell Throughput based Sleep Control Scheme for Heterogeneous Cellular Networks

The mobile traffic continuously grows at a rapid rate driven by the widespread use of wireless devices. Along with that, the demands for higher data rate and better coverage lead to increase in power consumption and operating cost of network infrastructure. The concept of heterogeneous networks (HetNets) has been proposed as a promising approach to provide higher coverage and capacity for cellular networks. HetNet is an advanced network consisting of multiple kinds of base stations, i.e., macro base station (MBS), and small base station (SBS). The overlay of many SBSs into the MBS coverage can provide higher network capacity and better coverage in cellular networks. However, the dense deployment of SBSs would cause an increase in the power consumption, leading to a decrease in the energy efficiency in downlink cellular networks. Another technique to improve energy efficiency while reducing power consumption in the network is to introduce sleep control for SBSs. This paper proposes cell throughput based sleep control which the cell capacity ratio for the SBSs is employed as decision criteria to put the SBSs into a sleep state. The simulation results for downlink communications demonstrate that the proposed scheme improves the energy efficiency, compared with the conventional scheme.

Flexible Coverage for Backhaul-Limited Ultradense Heterogeneous Networks: Throughput Analysis and $\eta$-Optimal Biasing

In fifth-generation ultradense heterogeneous network, backhaul plays an important role to provide connection to core networks for small base stations (SBSs). With the densification, nonideal backhaul will be deployed extensively, which constrains the throughput improvement due to the limited backhaul capacity. This paper models the SBSs and users as independent Poisson point processes and derives a semi-closed-form expression that analyzes the ergodic throughput of the network where SBSs have limited backhaul capacity. Based on analysis, to accomplish flexible coverage with limited backhaul, an optimization problem is formulated which accounts for cell association and aims at maximizing the number of offloading users while simultaneously minimizing the biasing factors and guaranteeing the received signal-to-interference-and-noise ratio of users when offloading. The optimization problem is shown to be a mixed-integer nonlinear problem, which can obtain a suboptimal solution by convex relaxation and further a distributed solution by problem decomposition via primal-dual method. Considering the complexity of ultradense network and practical implementation, a backhaulaware η-optimal biasing adjustment model is proposed for flexible coverage. This model aims to optimize the coverage for throughput improvement while matching the backhaul capacity. The simulation results show that average user and SBS throughput can be improved significantly by 2× when backhaul is constrained. Due to the transmit power and interference constraints on access link, there is no need to allocate too much bandwidth to backhaul, which provides guidance for self-backhaul resource allocation.

Uplink Spectral Efficiency Analysis of Decoupled Access in Multiuser MIMO HetNets

This paper explores decoupled access (DA) in terms of association probability and uplink (UL) spectral efficiency in multiuser multiple-input multiple-output (MIMO) heterogeneous networks. A maximum predicted SNR association policy is proposed for users with DA, which is shown to result in the nearly optimal UL spectral efficiency. The UL association probabilities are derived, where a higher probability of associating to lightly-loaded small base stations (BSs) is achieved by DA, leading to better load balancing in contrast to coupled access (CA). Then, the lower bounds on the UL spectral efficiency are derived based on the association probabilities, where interference is shown to be suppressed by multiple antennas at BSs. Moreover, an analytical comparison of the UL spectral efficiency between DA and CA reveals the superiority of DA over CA, which is more pronounced with the increasing transmit power of macro BSs and number of antennas equipped in small BSs. Finally, the results are validated by simulations.

Cell Association With User Behavior Awareness in Heterogeneous Cellular Networks

In heterogeneous cellular networks (HetNets) with macro base station (BS) and multiple small BSs (SBSs), cell association of user equipment (UE) affects UE transmission rate and network throughput. Conventional cell association rules are usually based on UE received signal-to-interference-and-noise-ratio (SINR) without being aware of other UE statistical characteristics, such as user movement and distribution. User behaviors can indeed be exploited for improving long-term network performance. In this paper, we investigate UE cell association in HetNets by exploiting both individual and clustering user behaviors with the aim to maximize long-term system throughput. We model the problem as a stochastic optimization problem, and prove that it is PSPACE-hard. For mathematical tractability, we solve the problem in two steps. In the first step, we investigate UE association for a specific SBS. We use a restless multiarmed bandit model to derive an association priority index for the SBS. In the second step, we develop an index enabled association (IDEA) policy for making the cell association decisions in general HetNets based on the indices derived in the first step. IDEA determines a set of admissible BSs for a UE based on SINR, and then associates the UE with the BS that has the smallest index in the set. We conduct simulation experiments to compare IDEA with other three cell association policies. Numerical results demonstrate the significant advantages of IDEA in typical scenarios.

Signal strength and spectral efficiency in heterogeneous ultradense networks

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

Cell Selection for Load Balancing in Heterogeneous Networks

The vision of advanced long-term evolution (LTE-A) project is set to ultimate increase of network capacity in heterogeneous networks (HetNets). In HetNets with small cell configuration, a considerable majority of user devices is eventually connected to the macrocell base station (MBS), while small base stations (BSs), such as femtocell access points (FAPs), are still without any user. This results in unbalanced load and reduces the data rate of macrocell user equipment (MUE). In this paper, a method is proposed for load balancing among FAPs, while desired throughput is achieved. The proposed method uses the estimated received signal strength from different BSs and adjusted pilot signals. Under the critical signal to interference plus noise ratio (SINR) condition, a list of candidate FAPs is prepared. The updated candidate list henceforth does not include the least visited FAPs, which in turn leads to lower unnecessary handoffs. Once the BS with the highest number of free RBs and the highest pilot signal power is selected, FAP allocates the RBs with higher SINRs (qualified RBs) to user. In the case of FAP unavailability, the algorithm compels users to connect to the MBS with adequate qualified RBs. The performance of the proposed method was evaluated under a variety of FAPs density, and the number and velocity of users in terms of throughput and Jain’s fairness index. The results evidence affordable improvements in the throughput and Jain’s index in comparison with other methods.

Coverage and Rate Analysis for Co-Existing RF/VLC Downlink Cellular Networks

This paper provides a stochastic geometry framework to perform the coverage and rate analysis of a typical user in co-existing visible light communication (VLC) and radio frequency (RF) networks. The framework can be customized to capture the performance of a typical user in various network configurations such as 1) RF-only , in which only small base-stations (SBSs) are available to provide the coverage to a user; 2) VLC-only , in which only optical BSs (OBSs) are available to provide the coverage to a user; 3) opportunistic RF/VLC , where a user selects the network with maximum received signal power; and 4) hybrid RF/VLC , where a user can simultaneously utilize the available resources from both RF and VLC networks. The developed model for VLC network precisely captures the impact of the field-of-view (FOV) of the photo-detector receiver on the number of optical interferers, distribution of the aggregate interference, association probability, the coverage probability, and average rate of a typical user. A closed-form approximation is presented for special cases and for asymptotic scenarios, such as when the intensity of SBSs becomes very low or the intensity of OBSs becomes very high. The closed-form solutions for network design parameters (such as intensity of OBSs and SBSs, transmit power, and/or FOV) enable network operators to distribute the users among RF and VLC networks according to their choice. Moreover, we also optimize the network parameters in order to prioritize the association of users to VLC network. Finally, simulations are carried out to verify the derived solutions. It is shown that the performance of VLC network depends significantly on the receiver’s FOV/intensity of SBSs/OBSs and careful selection of such parameters is crucial to harness the benefits of VLC networks. Important trade-offs between height and intensity of OBSs are highlighted to optimize the performance of VLC networks.

Dynamic association and participant based adaptive streaming in small base station for live video conference on 5G networks

AbstractTargeting to handle the explosive growth of mobile media streaming, the emerging fifth‐generation systems need to manage control plane and data plane appropriately. The control plane is used for node association in the heterogeneous cellular network and data plane for media streaming with heterogeneous user equipment. To satisfy the aforementioned requirements, we proposed a solution to alter the control plane and data plane by systematic usage of in‐network resource available in wireless edge (eg, macro, pico, and femto) small base stations (SBSs) for the deployment of smart association policies and media processing technique that improve the efficiency in fifth‐generation wireless networks. On the other hand, due to the broadcast nature of wireless medium, we introduce the media processing agent (MPA) that would reside in SBSs to process the media on the fly and then deliver streams adaptively to the participants. MPA can identify the presenter among the participant to create the server channel with media server and the observer can service from MPA itself. To better exploit the participant's node association with the nearby SBSs, association policies are enhanced with concerns on dynamic association with SBSs. In addition, we describe a heuristic algorithm that provides significant performance gains compared with the existing centralized processing schemes. Numerical results are presented with realistic parameters for the media processing speed, and the quality of delivery demonstrate up to 20% reduction in central processing unit usage over existing (centralized) media processing schemes.

Explicit Content Caching at Mobile Edge Networks with Cross-Layer Sensing.

The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of edge/fog computing applications. This paper proposes a novel edge-computing application called explicit caching, which stores selective contents at BSs and exposes such contents to local users for interactive browsing and download. We formulate the explicit caching problem as a joint content recommendation, caching, and delivery problem, which aims to maximize the expected user quality-of-experience (QoE) with varying degrees of cross-layer sensing capability. Optimal and effective heuristic algorithms are presented to solve the problem. The theoretical performance bounds of the explicit caching system are derived in simplified scenarios. The impacts of cache storage space, BS backhaul capacity, cross-layer information, and user mobility on the system performance are simulated and discussed in realistic scenarios. Results suggest that, compared with conventional implicit caching schemes, explicit caching can better exploit the mobile edge network infrastructure for personalized content dissemination.