Small Base Stations Research Articles

Analytical modeling of cache-enabled heterogeneous networks using Poisson cluster processes

The dual frequency Heterogeneous Network (HetNet), including sub-6 GHz networks together with Millimeter Wave (mmWave), achieves the high data rates of user in the networks with hotspots. The cache-enabled HetNets with hotspots are investigated using an analytical framework in which Macro Base Stations (MBSs) and hotspot centers are treated as two independent homogeneous Poisson Point Processes (PPPs), and locations of Small Base Stations (SBSs) and users are modeled as two Poisson Cluster Processes (PCPs). Under the PCP-based modeling method and the Most Popular Caching (MPC) scheme, we propose a cache-enabled association strategy for HetNets with limited storage capacity. The performance of association probability and coverage probability is explicitly derived, and Monte Carlo simulation is utilized to verify that the results are correct. The outcomes of the simulation present the influence of antenna configuration and cache capacities of MBSs and SBSs on network performance. Numerical optimization of the standard deviation ratio of SBSs and users of association probability is enabled by our analysis.

Fast Beam Discovery and Adaptive Transmission Under Frequency Selective Attenuations in Sub-Terahertz Bands

Sub-THz-based communication needs ultra-large antenna arrays to generate pencil-like beams under the stringent requirements of line-of-sight (LoS) to overcome the effect of the rapid attenuation of signal strength, including atmospheric attenuation. In addition, the complexity of attaining narrower beamwidth, which also requires fast and perfect alignment between the TX-RX pair, makes the sector sweeping-based beam discovery approach no longer suitable. To solve these challenging problems, in this paper we propose a leaky waveguide (LWG)-assisted, high-resolution codebook-based beam discovery, precoding, and combining scheme together with an adaptive frequency hopping spread spectrum (FHSS) for a network with relays and small base stations (SBS). The proposed method can help the transmitter quickly determine a suitable beam pattern for an LoS path requiring only limited feedback information. In the beam discovery phase, we use LWG to attain valuable information about the angle of departure (AoD) and angle of arrival (AoA). LWG has also been used to identify frequency selective attenuated regions, which can change from time to time depending on the local atmospheric moisture conditions. To mitigate the impact of such attenuations, we opt for an adaptive Frequency Hopping Spread Spectrum (FHSS). The simulation results indicate that our beam discovery and FHSS based method can achieve a much higher performance than baseline methods.

An Energy-Saving Scheme With Edge Computing and Energy Harvesting in mmWaves Backhauling HetNets

The exponential growth of data traffic from mobile devices requires the implementation of heterogeneous networks (HetNets), which densely deploy multiple radio access technologies to match such demands. The deployment of many small base stations (SBSs) leads to backhauling problems where wired backhauling is neither available nor efficient. millimeter waves (mmWaves) can potentially mitigate the backhauling problem by providing high throughput and low capital expenditure (CAPEX). However, due to the high attenuation rate in mmWaves, the increase in the distance between SBS and macro base station (MBS) can severely degrade the system’s overall performance. On the other hand, densely deployed SBSs with wireless backhauling can cause high energy consumption in the system. Therefore, in this work, a novel network model is presented in which a combination of SBS, active antenna units (AAUs), and edge computing units (ECUs) are deployed to minimize the overall energy consumption of the network while maintaining the sufficient quality of service (QoS). A mathematical model based on optimization modeling is introduced to solve user equipments (UEs) association, dynamic sleeping, backhauling and fronthauling, and power transmission. Due to the complexity of the formulated problem, a heuristic algorithm is introduced. Namely, integrated access and backhauling (IAB) with Edge Computing and Dynamic Sleeping Algorithm (IEDS) algorithm is introduced to decompose the formulated problem into two parts and solve them iteratively. Finally, computer simulation results that demonstrate the model’s performance are presented for comparison between optimal solution, IEDS, and HBDS, which shows that IEDS outperformed HBDS in performance with negligible computation difference.

Multi-User Dynamic Computation Offloading and Resource Allocation in 5G MEC Heterogeneous Networks with Static and Dynamic Subchannels

With the rapid development of Mobile Edge Computing (MEC) technology, the computationally intensive requests of end devices can be offloaded to MEC servers directly, which equipped at the edge of wireless networks. Through offloading, the performances such as the execution delay as well as the energy consumption can be effectively improved, which can significantly enhance the quality of user experience. Given the dynamics and randomness of computation requests arrival, the energy in the battery, the radio network environment, and the computation resource in the MEC server, it is a challenge to perform efficient offloading. Based on these problems, this paper proposes dynamic optimization schemes with queuing theory for the cases of the static subchannel and dynamic subchannel during a time slot separately in 5G MEC heterogeneous networks with multiple MDs equipped with the function of energy harvesting. In the schemes, offloading decisions and radio allocation strategies will be dynamically coordinated. They are also jointly allocated along with changing wireless communication resources and computation demands aiming to minimize the system average execution delay. Specifically, it is assumed that the offloading requests can be transmitted through either macro base stations or small base stations. In the case of the static subchannel, a joint resource allocation and computation offloading scheme based on Lyapunov optimization and Simulated Annealing Genetic Algorithm (SAGA) is put forward. As for the dynamic subchannel, the master-and-slave model is adopted and solved by SAGA and Sequential Quadratic Programming (SQP) method. At last, the effectiveness of the proposed schemes is verified through several simulations.

Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.

Non-Orthogonal Multiple Access for UAV-Aided Heterogeneous Networks: A Stochastic Geometry Model

In this work, we explore the potential benefits of deploying unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) with sub-6GHz bands and small cells terrestrial base stations (TBSs) with millimeter wave (mmWave) bands in a hybrid heterogeneous networks (HetNets). A flexible non-orthogonal multiple access (NOMA) based user association policy is proposed. By using the tools from stochastic geometry, new analytical expressions for association probability, coverage probability and spectrum efficiency are derived for characterizing the performance of UAV-aided HetNets under an appropriate Air-to-Ground (A2G) channel model and the Ground-to-Ground (G2G) channels with a LoS ball blockage model. Finally, we provide insights on the proposed hybrid HetNets by numerical results. We confirm that i) the proposed NOMA enabled HetNets is capable of achieving superior performance compared with the OMA enabled ABSs by setting power allocation factors and targeted signal-to-interference-plus-noise ratio (SINR) threshold properly; ii) there is a tradeoff between the association probabilities and the spectrum efficiency in the NOMA enabled ABSs tier; iii) the coverage probability and spectrum efficiency of the NOMA enabled ABSs tier is largely affected by the imperfect successive interference cancellation (ipSIC) coefficient, power allocation factors and SINR threshold; iv) compared with only sub-6GHz ABSs, mmWave enabled TBSs are capable of enhancing the spectrum efficiency of the HetNets when the mmWave line-of-sight (LoS) link is available.

AN OVERVIEW OF COMPONENTS FOR ENERGY EFFICIENT MULTIMEDIA NETWORKS BASED ON 5G RADIO ACCESS TECHNOLOGIES

Modern society is actively transitioning into an information-based one, and multimedia technologies have become an integral part of this process. Thanks to the proliferation of wireless access networks, users are becoming more mobile, and the development of the fifth generation of mobile networks, known as 5G, is a significant step in the advancement of information and communication technologies. 5G networks offer low latency and reliable connectivity, expanding the capabilities of mobile internet and machine communication. However, along with the opportunities provided by multimedia communication, there is a responsibility to consider the impact of associated technologies on the environment, as well as to address new challenges and the need for prudent resource utilization.The article defines the concept of "multimedia" and discusses various aspects of this concept, including digital storage and processing of information, components (text, photos, audio, and video), interactivity, and hypertextuality. It is noted that the transmission of multimedia data and the use of information technologies are closely linked to wireless access networks. The authors discuss challenges and solutions in the field of energy efficiency for networks. They provide statistics indicating that the information and communication technology (ICT) industry is responsible for a significant portion of global energy consumption and CO2 emissions, with radio access networks being a major contributor. Various components and technologies that can contribute to the development of energy-efficient multimedia networks based on 5G radio access technologies are examined. Specifically, heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are highlighted as key components for achieving energy efficiency.The importance of using heterogeneous networks to reduce the distance between transmitters and receivers is emphasized, along with the possibility of putting small base stations into sleep mode when there is no network load. Technologies like NOMA and MIMO are discussed as crucial components for achieving spectral efficiency and energy efficiency.Additionally, the article focuses on wireless sensor networks (WSNs) and suggests ways to optimize them for energy efficiency. This includes operating sensor units only when necessary, implementing wireless charging, using energy-efficient optimization methods, and applying efficient routing schemes. The authors also highlight the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Methods such as using renewable energy sources, increasing the energy efficiency of hardware, and implementing energy-efficient routing are discussed. In conclusion, the article underscores the importance of energy efficiency and reduced CO2 emissions in modern multimedia networks, particularly in the context of 5G networks. It calls for interdisciplinary efforts to address these critical challenges in the field of information and communication technologies.

Message-Passing Based User Association and Bandwidth Allocation in HetNets With Wireless Backhaul

This work presents a joint design of user association and resource allocation in a heterogeneous network, which is comprised of a single macro base station and a group of small base stations interconnected through wireless backhaul. In such a configuration, we optimize user association and resource allocation so that the total sum of generalized utilities is maximized. This problem is cast as a combinatorial formulation with a fractional objective. To handle this design challenge, we develop a novel message-passing framework to obtain an efficient joint autonomous solution for user association and resource allocation. The simulation results show that the proposed algorithm outperforms existing techniques with various network utility functions.

Broadband Dual-Polarized Magnetoelectric Dipole Antenna With Compact Structure for 5G Base Station

This paper presents a compact wideband dual-polarized magnetoelectric dipole antenna suitable for 5G base stations, which can cover 5G NR n77/78/79 band. The proposed antenna is configured with a cross ME dipole loaded with a rectangular slot, and two L-shaped feed lines are coplanar with the vertical patch, which makes the transverse size of the antenna more compact. The prototype size is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.57\,\,\lambda _{min} \times 0.57\,\,\lambda _{min}$ </tex-math></inline-formula> (length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> width). According to the measured results, the operating band of the antenna is 3.06-5.28 GHz, and the impedance bandwidth is 53.2% of −10 dB. In the whole frequency range, the isolation between ports is less than −22 dB. Furthermore, metal baffles around the ground are used to stabilize the radiation pattern and improve the front-to-back ratio, achieving gains greater than 8dBi in the frequency band. However, the half power beam width (HPBW) exceeds 65°±5° for base station applications. The antenna has the advantages of broadband, high front-to-back ratio and low cross-polarization, and can be used as a good candidate antenna for 5G commercial small base stations.

Handover Decision Making for Dense HetNets: A Reinforcement Learning Approach

In this paper, we consider the problem of decision making in the context of a dense heterogeneous network with a macro base station and multiple small base stations. We propose a deep Q-learning based algorithm that efficiently minimizes the overall energy consumption by taking into account both the energy consumption from transmission and overheads, and various network information such as channel conditions and causal association information. The proposed algorithm is designed based on the centralized training with decentralized execution (CTDE) framework in which a centralized training agent manages the replay buffer for training its deep Q-network by gathering state, action, and reward information reported from the distributed agents that execute the actions. We perform several numerical evaluations and demonstrate that the proposed algorithm provides significant energy savings over other contemporary mechanisms depending on overhead costs, especially when additional energy consumption is required for handover procedure.

From Architecture to Field Trial: A Scheme of mmWave Based IAB and Small Base Station of Commercial Frequency for the Communication of Subway Tunnel Scenario

From Architecture to Field Trial: A Scheme of mmWave Based IAB and Small Base Station of Commercial Frequency for the Communication of Subway Tunnel Scenario

Blockchain-escorted distributed deep learning with collaborative model aggregation towards 6G networks

The 6th generation (6G) network targets the Internet of Everything (IoE) implementation, and Distributed Deep Learning (DDL) can promote this progress with innovative performance in generating intelligence. Meanwhile, the 6G networks support ultra-reliable and low-latency communication (uRLLC) and thus can further elevate the DDL performance to empower the IoE development. However, DDL designs mostly focus on individual areas and yield separate intelligence which is insufficient for the IoE; besides, DDL platforms are usually managed in the centralized fashion, which is vulnerable for data preservation and task execution; the complexity of 6G networks involving heterogeneous devices and relations aggravates issues about reliability and efficiency of DDL. To this end, we propose a novel BC-escorted 6G-based DDL design for trustworthy model training. In this system, the 6G network design is utilized for system-wide uRLLC; non-homogeneous edge devices are grouped up with weighted consideration for DDL to train CNN models; macro base stations (MBSs) and small base stations (SBSs) jointly provide two-tiers parameter aggregation to elevate the knowledge level; a dual-driven BC consensus is designed to verify tasks and models; users anyplace can retrieve models via the BC nodes for object detection. The proposed design is evaluated in comparison with Cloud-based and P2P-based DDLs, and the results demonstrate better performance on accuracy and latency achieved in the proposed system.

Joint Optimization Algorithm for Small Base Station States Control and User Association in Wireless Caching Networks

The energy consumption management of small base stations (SBSs) in wireless caching networks with dense deployment of SBSs is an urgent problem to be solved. This paper jointly optimizes the SBS state control and user association problems in caching networks to reduce network energy consumption while taking into account the average service latency of the network to ensure user experience. First, a new definition of three-state SBSs in caching networks is proposed based on their ability to keep content cache updated. Then, a relaxed threshold setting method is designed and the SBS traffic prediction is used to obtain the initial state information of SBSs in the next period. In order to eliminate the impact on the accessing users when the switched-off base station (BS) wakes up, a SBS state asynchronous switching mechanism is proposed to ensure that the users who switch to the waking SBS can carry out communication services normally, and a user association strategy is constructed with the SBS load as the optimization target. Finally, a joint optimization model of user association and SBS state control (SSC-UA) is constructed to admit and correct the initial state of SBSs to maximize the system gain and obtain the final state strategy for each SBS in the next period. The simulation results show that the proposed SSC-UA algorithm can effectively improve the energy efficiency and reduce the network service delay at the same time.

Local Delay Analysis and Improvement for User-SBS Correlated Deployments With Random DTX

Stochastic geometry has emerged as a well-established analysis tool for dense wireless networks. However, the commonly assumed homogeneous Poisson point process (PPP) model, where users are distributed in the space uniformly, may not be accurate enough, since the users are more likely to be clustered at densely populated sites. Driven by capacity needs, small base stations (SBSs) are deployed at the user hotspots, which results in a user-SBS correlated deployment. In order to capture this correlation, user locations are modeled as Poisson cluster process (PCP) with SBSs serving as the cluster centers. In this paper, by using the discontinuous transmission (DTX) mode, the local delay has been analyzed. We carried out the analysis under two classic PCP models—Thomas cluster process and Matérn cluster process. Moreover, it is found that under PCP the local delay is more dependent on base station (BS) density and transmit power than under the conventional PPP model. Finally, we found that the local delay increases as the cluster size increases, and it converges to the PPP result when the cluster size approaches the infinity.

Caching in Narrow-Band Burst-Error Channels via Meta Self-Supervision Learning

We consider a coordinated small-cell network in narrow-band deep fading channels, where the transmissions from small base-stations (SBSs) to users usually encounter burst errors. Each SBS has limited cache-capacity via which partial contents can be cached to avoid being fetched from remote cloud and hence reduce the transmission delay. However, due to deep fading, the content transmission may occur numerous burst errors. To solve this problem, in this letter, a meta-based self-supervision learning method is introduced to find the optimal cache policy for minimizing the burst errors. In contrast to the traditional methods, the proposed one has better performance and learning efficiency. More particularly, the proposed can determine much proper cache policy by just training with a few samples of average fading duration of each channel. We demonstrate the effectiveness of the proposed method by numerical simulations which indicate that the proposed meta-learning method is superior to common existing methods in combat of the burst-error problem regarding convergence time and accuracy.

SINR Meta Distribution for mmWave Heterogeneous Networks Under Varying Queue Status: A Spatio-Temporal Analysis

A fine-grained analysis of network performance is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as a Poisson point process (PPP). By utilizing stochastic geometry and queueing theory, we characterize the spatial and temporal randomness while the special characteristics of mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming, are incorporated into the analysis. We derive the moments of the conditional successful transmission probability (STP). By taking the temporal random arrival of traffic into consideration, an equation on the meta distribution is formulated and its solution is obtained in a recursive manner. The critical arrival rate for a fraction of the queues at the BSs to be stable is provided. In addition, the mean delay is obtained. The numerical results reveal the impact of the key network parameters, such as the blockage parameter, the bias factor, the number of antenna elements and the BS density, on the network performance. Specifically, offloading the users from the macro BS tier to the small BS tier by biasing can reduce the performance fluctuation while only causing a slight degradation of the overall network performance.

A novel power consumption optimization framework in 5G heterogeneous networks

The fifth-generation (5G) mobile networks have the capacity to handle the dynamic traffic demands of the user equipment (UE). One approach is the dense deployment of small base stations (s-BSs), which can control the dynamic traffic demands. We consider s-BSs which are interconnected through the mmWave backhaul (BH) link to transfer traffic from the s-BS to the core network through the macro base station (MBS). In this setting, the network power consumption is affected by the way UEs are connected to the base stations and the traffic routes through the BH links. The main objective of this paper is to minimize the power consumption of the heterogeneous network (HetNet) with the intelligent backhauling and s-BS/BH link sleeping (IBSBS) framework. The proposed framework provides the minimized power consumption in HetNets through UE association, backhauling, and sleep s-BS/BH links. The UE association and backhauling uses a heuristic function based intelligent backhauling algorithm to assess the minimum power consumption from the s-BS to the MBS while considering the power and capacity constraints of the network. The load sharing based s-BS sleeping algorithm dynamically changes the states (active/sleep) of the s-BSs according to their loads without compromising UE demands. Different distributions of UEs and different data rates over the HetNets architecture are considered for performance evaluation. The evaluation results of the proposed framework outperform the state-of-the-art algorithms in terms of network energy efficiency, power consumption, and the number of active s-BSs/BH links.

Total Delay Optimization in Cache-Enabled C-RANs With Hierarchical Rate Splitting

This paper proposes a Hierarchical Rate Splitting (HRS)-based and Macro Base Station (MBS)-assisted cooperative content delivery scheme for Cache-enabled Cloud Radio Access Networks (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RANs). In this scheme, a user is served by a cluster of Small Base Stations (SBSs) that cache contents. If the user-requested content is missing in its cache, the SBS retrieves it from the central processor (backhaul link) and the MBS (wireless broadcast). We minimize the total delay of this system. We thus optimize user grouping, decoding order, RS variables, BS clustering, and beamforming subject to the transmit power and backhaul capacity constraints. This process formulates a non-convex problem, which we decouple into three subproblems. First, we must pair users to reduce intra-group interference. We thus develop a Low-Complexity User Pairing (LCUP) algorithm. Second, we optimize the decoding order to minimize the broadcast link delay. We, therefore, propose a Dynamic Delay-based Decoding Order Update (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> OU) algorithm. Third, we jointly design the RS variables, beamforming, and BS clustering. We use surrogate optimization via the Quadratic Transform (QT), Woodbury matrix identity, and Taylor expansion. We then recast this problem to a Two-Tier Alternating (TTA) problem, which we solve with closed-form (outer tier) and convex (inner tier) formulations. We integrate these algorithms to propose an overall algorithm. It achieves significant performance gains and a 3-fold reduction in time-complexity over the standard Semi-Definite Relaxation (SDR)-based algorithm and several benchmark schemes.

Risk Area Alert Through Heterogeneous Mobile Networks: A New Approach to Fight COVID-19 and Beyond

COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this article, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID-19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end.

QoE-Driven Distributed Resource Optimization for Mixed Reality in Dynamic TDD Systems

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.