Macrocell Base Station Research Articles

Performance analysis of 5G heterogeneous networks under the impact of aggregate interference over Nakagami‐m fading channels

AbstractHeterogeneous networks (HetNets), which consist of ultra‐dense femtocell networks underlaid by traditional homogenous macrocell networks, are a promising option for the exceedingly high data rate demands of upcoming 5G communications. The densely deployed femtocells in cochannel mode cause severe interference because of the short distances between cochannel cells, which is the main challenge for femtocell deployment in HetNets. The HetNets are modeled based on stochastic geometry, which is assumed to distribute the interfering nodes as a point process. Specifically, in this article, a homogeneous Poisson point process is used to model the random distribution of macrocell users and femtocell base stations. Based on the complementary cumulative distribution function of the received signal to interference ratio at the macrocell base stations and femtocells, closed‐form expressions are derived for the outage probability. Further, closed‐form expressions are derived for the network throughput and energy efficiency of femtocells. Finally, theoretical and simulation results are discussed for the derived expressions under different system parameters.

POTAM: A Parallel Optimal Task Allocation Mechanism for Large-Scale Delay Sensitive Mobile Edge Computing

Design an optimization model for task management among Mobile Terminal (MT), Macro cell Base Station (MBS), and multiple Small cell Base Stations (SBS) for the large-scale Mobile Edge Computing (MEC) system, is a challenging issue due to the large number of tasks and SBSs. Inspired by this, we propose a Parallel Optimal Task Allocation Mechanism (POTAM) framework for MEC, which includes Device to Device (D2D)-enabled computing, MBS computing and Edge Computation Resource Distribution (ECRD) computing. In POTAM, we exploit a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of delay and energy consumptions, which formulates the task allocation under these requirements as a nonlinear 0–1 integer programming problem. To solve this problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing and Cyclic Block coordinate Gradient Projection (CBGP) methods, which can guarantee convergence and reduce computational complexity with a good scalability. In order to allocate task cooperatively, an optimal approach is proposed, ECRD-A, which is used to find the shortest path among each node. Numerical results demonstrate the effectiveness of the POTAM and it can effectively reduce delay and energy consumption for a large-scale MEC system.

Performance Analysis of Heterogeneous Cellular Caching Networks With Overlapping Small Cells

Caching at network edges has attracted more and more research interests recently for the purpose of alleviating the network traffic pressure especially in backhaul links and improving user experience. We study Heterogeneous Cellular Caching Networks (HCCNs) consisting of macro cells in which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> small cell base stations (SBSs) equipped with cache memory operate in conjunction with the macro cell base station (MBS). We provide closed-form expressions of the MBS and SBSs utilization factors and average user-experienced-delay in HCCNs with overlapping coverage regions, considering general traffic models for the request arrivals based on the Independent Reference Model (IRM) and renewal traffic models. Moreover, we propose a novel caching scheme in HCCNs, namely Cooperative Most Popular Caching (CMPC), which outperforms the existing schemes in terms of delay. Subsequently, we present the bandwidth assignment problem aiming to minimize the average user-experienced-delay under stability and cache size constraints in HCCNs with overlapping coverage regions and stochastic request arrivals. Finally, the analytic results are validated through numerical results and real trace-driven experiments.

Incentive Mechanism Design for Two-Layer Wireless Edge Caching Networks Using Contract Theory

Wireless caching technologies have been proposed to relieve the transmission pressures, especially, the transmission redundancy on back-haul channels. In this paper, we consider a two-layer caching network, consisting of traditional macro-cell base station (MBS) aided back-haul channels and small-cell base stations (SBSs) aided local links. The network service provider (NSP), who is in charge of the two layers, leases its resources of the secondary layer, i.e., coverage of the SBSs, to content providers (CPs) for making extra profits and releasing pressures on the back-haul channels. At the same time, CPs will evaluate whether they are provided with proper incentives to pre-cache their files in the SBSs. Considering different quality of services (QoS) provided by the two layers as well as the economical impact of the traditional layer on the secondary layer, the NSP designs the optimal incentive mechanisms within the framework of contract theory for maximizing its own profits. First, we formulate the utility of the NSP and CPs. Then, the minimum transmission requirement, reserve price and limited resources are considered as constraints in designing the optimal contract. Also, some important properties of these constraints are analyzed to facilitate the optimal contract determination process. At last, an optimal contract determination scheme is proposed, based on which the optimal coverage set is determined first, and then the corresponding optimal prices are derived with the aid of equal cost line. Numerical results are provided to demonstrate the effectiveness of the proposed optimal contract in increasing the NSP's profits and incentivizing CPs to transmit on the secondary layer.

Joint Optimization for Traffic-Offloading and Resource-Allocation Over RF-Powered Backscatter Mobile Wireless Networks

We develop the joint optimization schemes for traffic-offloading and resource-allocation over radio-frequency (RF) powered backscatter-based mobile wireless networks, where a macro-cell base station (BS), several small-cell access points (SAPs), and multiple energy harvesting (EH) mobile users (MUs) co-exist. Optimizing MUs' network access (via traffic-offloading) and system resource-allocation, we aim to minimize MUs' energy consumption by using low-energy consumption of backscatter. First, we consider the scenarios when short-range ambient backscatter (AB) communication (e.g., over several meters) is employed, enabling MUs to access nearby SAPs via ambient backscatter RF signals for data transmission. Using the alternating directions method of multipliers (ADMM), we develop a distributed traffic-offloading and resource-allocation scheme. Then, we focus on traffic-offloading and resource-allocation by adopting the concurrent AB (CAB) transmission, where multiple MUs can backscatter data concurrently to further reduce energy consumption. Moreover, we also use long-range bi-static backscatter (BB) communication (e.g., over 270-meter distance) for data transmission between MUs and BS, and then MUs can convey data by backscattering RF signals radiated from dedicated carrier emitters. Then, we study the joint traffic-offloading and resource-allocation when AB and BB communications are adopted by MUs accessing SAPs and BS, respectively. Finally, the numerical analyses validate and evaluate our developed schemes, showing their good convergence performances and significant reduction in energy consumption of MUs through backscatter communications.

A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

&lt;span lang="EN-US"&gt;Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, &lt;/span&gt;&lt;span lang="EN-US"&gt;multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.&lt;/span&gt;

Joint sub-carrier allocation and 3D beamforming design in OMA-NOMA based mmWave heterogeneous networks under channel uncertainties

In this paper, we investigate a new radio resource allocation, three-dimensional (3D) multiple input single output (MISO) beamforming and cross-tier interference mitigation in a millimeter wave (mmWave) heterogeneous cellular network. To manage the cross-tier interference, a scheme based on the combination of orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) is proposed. To this end, the total bandwidth is divided into OMA-based and NOMA-based sub-carriers in which the macro-cell base station (MBS) uses OMA-based sub-carriers and the total bandwidth is considered for small cell base stations (SBSs). For maximizing the sum-rate of OMA-NOMA based system, a non-convex optimization problem is formulated where the channel state information (CSI) is not known perfectly. The proposed optimization problem is solved by introducing auxiliary variables, alternate search method (ASM) and successive convex approximation (SCA) technique. Then, an iterative algorithm is adopted where sub-carrier, beamforming and BS tilt angle sub-problems are sequentially obtained. The simulation results confirm the effectiveness of the proposed scheme compared with 2D beamforming and OMA-based schemes.

Interference mitigation using antenna selection for the heterogeneous networks

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.

Extension of Modified Joint Uplink and Downlink Scheduling Algorithm for LTE-A in Femtocell Networks

Aim: The 5G LTE-Advanced (LTE-A) intended to provide increased peak data rates for the mobile users with the use of Carrier Aggregation (CA) technology. Due to need of uninterrupted bi-directional communication between the eNodeB and User Equipment (UE) in LTEA, Joint Scheduling Algorithm is considered as central research topic. Objective: A modified joint Uplink/ Downlink (UL/DL) Scheduling algorithm to meet on demands service request from the UEs is proposed in this paper. Methods: CA is used for calculate the weight factors for the bandwidth allocation among the mobile users based on the QoS Class Identifier (QCI). However due the huge amount of data flow in the indoor coverage yield introduction of the small cell called femtocells. Femtocells are randomly deployed in macro cell area in order to improve indoor coverage as well capacity enhancement. Result: Mixed types of traffic are considered ranging from real time to non real time flows and quality of service is evaluated in term of throughput, packet loss ratio, fairness index and spectral efficiency. The proposed modified joint user scheduling algorithm results better in delay among the end users due the reduction in the traffic load of the macro cell base station. Conclusion: Simulation results shows that, the proposed methodology suits best for the small scale network architecture with increased spectral efficiency and throughput among the UEs.

An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users’ (PUs’) (or licensed users’) activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance; or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs’ activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry Pi3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner; and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs’ activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.

On the Fundamental Limits of Fog-RAN Cache-Aided Networks With Downlink and Sidelink Communications

Maddah-Ali and Niesen (MAN) in 2014 showed that coded caching in single bottleneck-link broadcast networks allows serving an arbitrarily large number of cache-equipped users with a total link load (bits per unit time) that does not scale with the number of users. Since then, the general topic of coded caching has generated enormous interest both from the information theoretic and (network) coding theoretic viewpoint, and from the viewpoint of applications. Building on the MAN work, this paper considers a particular network topology referred to as cache-aided Fog Radio Access Network (Fog-RAN), that includes a Macro-cell Base Station (MBS) co-located with the content server, several cache-equipped Small-cell Base Stations (SBSs), and many users without caches. Some users are served directly by the MBS broadcast downlink, while other users are served by the SBSs. The SBSs can also exchange data via rounds of direct communication via a side channel, referred to as “sidelink”. For this novel Fog-RAN model, the fundamental tradeoff among (a) the amount of cache memory at the SBSs, (b) the load on the downlink (from MBS to directly served users and SBSs), and (c) the aggregate load on the sidelink is studied, under the standard worst-case demand scenario. We propose a converse bound whose key novelty is to jointly bound the downlink load an the sidelink load. For the achievability, by leveraging the network topology, we propose two classes of memory-loads point, where the SBS sidelink load is minimum and the MBS downlink load is minimum, respectively. By memory-sharing between these two classes of memory-loads points, some exact or order optimality results are obtained. Several existing models (e.g., Device-to-Device coded caching, single bottleneck-link coded caching with shared caches, single bottleneck-link caching coded caching with cache-less users) are recovered as special cases of this network model and by-product results of independent interest are given. Finally, the role of topology-aware versus topology-agnostic caching is discussed.

A Game Theoretic Scheme for Collaborative Vehicular Task Offloading in 5G HetNets

The 5G heterogeneous networks (HetNets) are capable of providing real-time computing services for autonomous vehicles (AVs) by deploying edge computing devices (ECDs) at macro cell base stations (MCBSs) and small cell base stations (SCBSs). With the imbalanced distribution and fast moving AVs contending intensely for computing services, how to efficiently exploit cooperations among participants in 5G HetNets to improve the service performance is therefore challenging. In this paper, we develop a game theoretic scheme for collaborative vehicular task offloading to facilitate the computing services in 5G HetNets. Specifically, we propose a two-stage vehicular task offloading mechanism to promote the cooperation among participants with the target of improving the task completion rate and the utilities of the participants, where the mechanism jointly considers the network architecture of the HetNets, the imbalanced distribution of AVs and the reuse of task results. In the first stage, an auction model is designed to help the MCBS select the optimal SCBS to execute the offloaded task based on the requirement of the task and the available computing resources of SCBSs. According to the task execution cost declared by the selected SCBS, the MCBS then bargains with the AV for the agreement of the task offloading service to maximize their utilities in the second stage. Using simulations, we show that the proposed collaborative task offloading scheme can achieve a higher task completion rate for the task offloading service and bring higher utilities to all participants than conventional schemes.

Joint Optimal Multicast Scheduling and Caching for Improved Performance and Energy Saving in Wireless Heterogeneous Networks

Base station caching and multicast are two promising methods to support mass content delivery in future wireless network environments. However, existing scheduling designs do not take full advantage of the two methods. This article focuses on employing multicast scheduling and caching in a network architecture which involves both macro cell base stations (MBS) and small cell base stations (SBS) in order to achieve joint optimization of average delay and power consumption. We describe this co-optimization problem as the Multicast-Aware Caching Scheduling Problem (MACSP). This article proposes a novel pending request queue model, which aims to solve the problem of long waiting time for non-popular content, and transform this collaborative multicast-cache scheduling problem into a Markov Decision Process that can be solved using reinforcement learning methods. For actual deployment, the paper further introduces a Distributed Policy Gradient algorithm (DPG) with similar performance and lower complexity. The simulation-based testing results demonstrate that our model and algorithm have better performance and lower energy consumption than existing state-of-the-art approaches.

Modeling and Analyzing of Millimeter Wave Heterogeneous Cellular Networks by Poisson Hole Process

In this paper, a novel Poisson hole process (PHP) modeling of wireless networks is proposed. Contrary to the prior PHP models with circular-shaped holes, we utilized circular sector holes in a random direction to capture the spatial separation between tiers in a millimeter wave (mmWave) heterogeneous cellular network (HCN). In this case, small cell base stations and macrocell base stations are distributed as a PHP and Poisson point process (PPP). Using tools from stochastic geometry, we derive approximate analytical expressions by regarding the effect of one or several holes on coverage probability. Simulation results reveal that compared to conventional PPP-modeling of HCNs, the proposed approaches can provide about 2–3 dB more accurate analysis in terms of signal-to-interference-and-noise ratio coverage probability. Moreover, some interesting insights about the effect of holes on coverage probability and the relation between proposed hole configuration and prior models with circular holes is discovered. It turns out that the analysis based on the proposed PHP model can provide a more general study than prior works.

Noncoherent Joint Transmission Beamforming for Dense Small Cell Networks: Global Optimality, Efficient Solution and Distributed Implementation

We investigate the coordinated multi-point noncoherent joint transmission (JT) in dense small cell networks. The goal is to design beamforming vectors for macro cell and small cell base stations (BSs) such that the weighted sum rate of the system is maximized, subject to a total transmit power at individual BSs. The optimization problem is inherently nonconvex and intractable, making it difficult to explore the full potential performance of the scheme. To this end, we first propose an algorithm to find a globally optimal solution based on the generic monotonic branch reduce and bound optimization framework. Then, for a more computationally efficient method, we adopt the inner approximation (InAp) technique to efficiently derive a locally optimal solution, which is numerically shown to achieve near-optimal performance. In addition, for decentralized networks such as those comprising of multi-access edge computing servers, we develop an algorithm based on the alternating direction method of multipliers, which distributively implements the InAp-based solution. Our main conclusion is that the noncoherent JT is a promising transmission scheme for dense small cell networks, since it can exploit the densitification gain, outperforms the coordinated beamforming, and is amenable to distributed implementation.

Low-Resolution ADC Quantized Full-Duplex Massive MIMO-Enabled Wireless Backhaul in Heterogeneous Networks Over Rician Channels

This paper studies the spectral/energy efficiency (SE/EE) of a heterogeneous network with the backhaul enabled by low-resolution analog-to-digital converters (ADCs) quantized full-duplex massive multiple-input multiple-output (MIMO) over Rician channels. Backhaul communication is completed over two phases. During the first phase, the macro-cell (MC) base station (BS) deploys massive receive antennas and a few transmit antennas; the small-cell (SC) BSs employ large-scale receive antennas and a single transmit antenna. For the second phase, the roles of the transmit and receive antennas are switched. Due to the low-resolution ADCs, we account for quantization noise (QN). We characterize the joint impact of the number of antennas, self-interference, SC-to-SC interference, QN, and Rician $K$ -factor. For the first phase, the SE is enhanced with the massive receive antennas and the loss due to QN is limited. For the second phase, the desired signal and QN have the same order. Therefore, the SE saturates with the massive transmit antennas. As the Rician $K$ -factor increases, the SE converges. Power scaling laws are derived to demonstrate that the transmit power can be scaled down proportionally to the massive antennas. We investigate the EE/SE trade-offs. The envelope of the EE/SE region grows with increase in the Rician $K$ -factor.

Analysis and Optimization of Service Delay for Multiquality Videos in Multitier Heterogeneous Network With Random Caching

Aiming to minimize service delay, we propose a new random caching scheme in device-to-device (D2D)-assisted heterogeneous network. To support diversified viewing qualities of multimedia video services, each video file is encoded into a base layer (BL) and multiple enhancement layers (ELs) by scalable video coding (SVC). A super layer, including the BL and several ELs, is transmitted to every user. We define and quantify the service delay of multi-quality videos by deriving successful transmission probabilities when a user is served by a D2D helper, a small-cell base station (SBS) and a macro-cell base station (MBS). We formulate a delay minimization problem subject to the limited cache sizes of D2D helpers and SBSs. The structure of the optimal solutions to the problem is revealed, and then an improved standard gradient projection method is designed to effectively obtain the solutions. Both theoretical analysis and Monte-Carlo simulations validate the successful transmission probabilities. Compared with three benchmark caching policies, the proposed SVC-based random caching scheme is superior in terms of reducing the service delay.

Using LTE-Sim in New Hanover Decision Algorithm for 2-Tier Macrocell-Femtocell LTE Network

Deployment of mini macrocell base stations can also be referred to as femtocells improve quality of service of indoor and outdoor users. Nevertheless, mobility management remains a key issue with regards to their deployment. This paper is leaned towards this issue, with in-depth focus on the most important aspect of mobility management - handover. In handover management, making a handover decision in the LTE two-tier macrocell femtocell network is a crucial research area. Decision algorithms in this research, are classified and comparatively analyzed according to received signal strength, user equipment speed, cost function and interference. However, it was observed that most of the discussed decision algorithms fail to consider cell selection with hybrid access policy in a single macrocell multiple femtocell scenario, another observation was a majority of these algorithms lack the incorporation of user equipment residence parameter. Not including this parameter boosts the number of unnecessary handover occurrence. To deal with these issues, a sophisticated handover decision algorithm is proposed. The proposed algorithm considers the user’s velocity, received signal strength, residence time as well as the femtocell base station’s access policy. Simulation results have shown that the proposed algorithm reduces the number of unnecessary handovers when compared to conventional received signal strength based handover decision algorithm.

Joint resource block and power allocation in heterogeneous cellular networks with different backhaul capacity limitations

AbstractIn heterogeneous cellular networks (HCNs), the growing demand of small cell data traffic puts great pressure on the capacity limited backhaul links. Resource allocation is an efficient approach to mitigate the severe interference and improve the network performance in HCNs. However, differentiated backhaul capacity constraints introduce the inherent nonconvexity to the resource optimization problem, which hinders the development of the optimal solutions. In this article, we investigate the joint resource block (RB) and power allocation problem under two different backhaul capacity limitations. First, we consider the small cell backhaul limitation scenario where the backhaul capacity constraint is imposed on each small cell. Then, we extend the resource optimization to the macrocell base station backhaul limitation scenario which considers the overall backhaul capacity constraint on the whole small cell network. Besides, the quality of service (QoS) requirements of users and the cross‐tier interference mitigation are also considered. This resource allocation problem is formulated as a nonconvex mixed‐integer nonlinear programming (MINLP) problem. By variable substitution and constraints relaxation, we convert the original optimization problem into a convex one and obtain the optimal solutions. Accordingly, joint RB and power allocation algorithms are proposed. Numerical results verify the effectiveness of the proposed algorithms and evaluate the impact of different types of backhaul capacity limitations on system performance.

Rate Maximization for Hybrid Access Femtocell Networks With Outage Constraints Based on Pricing Incentive Mechanism

Wireless services are developing rapidly and user requirements also are becoming more demanding. Femtocell networks have great potential in many aspects, especially in expanding the capacity and increasing the coverage of traditional cellular networks. However, it is challenging to motivate femtocell base stations (FBSs) to voluntarily adopt a hybrid access strategy to provide services and access to macrocell users (MUEs). In order to maximize the transmission rate of users, we propose an incentive mechanism in two-tier femtocell networks including a macrocell and multiple femtocells that share the spectrum. In the proposed mechanism, the macrocell base station (MBS) attempts to provide femtocells with a reward by pricing the transmission rates of the accessed MUEs, which are guaranteed by the FBSs. A Stackelberg game is developed to maximize the utility of the MBS and FBSs, and to guarantee the quality of service (QoS) of different users. An uncertain channel gain is formulated in the optimization problem and the uncertainty is restricted by a probabilistic constraint. Furthermore, a power control and nonuniform price bargaining algorithm is proposed to satisfy practical operation and obtain an optimal solution. Finally, numerical results validate the effectiveness of the proposed algorithm in terms of transmission rate improvement and outage reduction.