Small Base Stations Research Articles

Interference mitigation in intentional jammers aided non-uniform heterogeneous cellular networks.

Coverage and capacity are optimized in fifth generation (5G) networks by small base station (SBS) distribution in the coverage realm of macro base station (MBS). However, system performance is significantly reduced by inter-cell interference (ICI) because of the orthogonal frequency division multiple access assumption. In addition to ICI, this work considers intentional jammers' interference (IJI) due to the presence of jammers. These Jammers try to inject undesirable energies into the legitimate communication band, which significantly degrade uplink (UL) signal-to-interference ratio (SIR). To reduce ICI and IJI, in this work, we employ SBS muting, where the SBSs near MBS are switched off. To further mitigate ICI and IJI, we use one of the effective interference management schemes a.k.a reverse frequency allocation (RFA). We presume that due to mitigation in ICI and IJI, the UL coverage performance of the proposed network model can be further improved.

Energy-Efficient User Association in mmWave/THz Ultra-Dense Network via Multi-Agent Deep Reinforcement Learning

As a key enabler for 5G and 6G wireless communications, millimeter-wave (mmWave) and terahertz (THz) ultra-dense network (UDN) has received a great deal of attention recently. The mmWave/THz UDN can greatly improve the capacity of cellular network by exploiting the reduced path loss of densified network and the wide range of frequency spectrum. In order to maximize the capacity gain of UDN, the associations between small base stations (SBSs) and users should be designed deliberately. However, since computational complexity and signaling overhead increase exponentially with the numbers of SBSs and users, it is very difficult to apply the conventional centralized approaches in the UDN. In this paper, we propose a decentralized user association technique based on multi-agent actor-critic (AC) to maximize the energy efficiency of UDN. In our technique, actor network determines the user association of the SBS using the local observation and critic network informs the energy-efficient user association decision to the actor network. In doing so, the DRL agent in each SBS can find out the user association decision maximizing the network energy efficiency. From the extensive simulations, we demonstrate that the proposed scheme achieves more than 50 % average energy efficiency gain over the conventional user association techniques.

Social-Aware Graph-Based Collaborative Caching in Edge-User Networks

The soaring data traffic brings tremendous pressure on core networks. Collaborative caching among edge servers and user devices has been regarded as a promising technology for releasing backhaul pressure and reducing content download delay in the next-generation communication network. However, given the capacity constraint of edge servers and caching devices, a challenge is how to exploit these storage resources efficiently. In this paper, we introduce a three-tier collaborative caching architecture for edge-user networks and propose a social-aware graph-based collaborative caching (SGCC) strategy to minimize content download delay. Specifically, the small base station tier and user equipment tier are modeled as a one-tier directed graph. Moreover, we model the physical attributes and social attributes as the weight of the directed edges in the physical link graph and social graph, respectively. Then, we construct a social-aware graph by merging the two-tier topology of the physical link graph and social graph to simplify the caching decision. Further, we design SGCC to cache the most popular content according to the weighted content popularity. Simulation results demonstrate that the proposed SGCC can effectively reduce the average download delay and has a 26.8% lower average download delay, compared with the uniform popular caching baseline.

Active frequency selective surface-based 360° beam steering in two switchable frequency bands independently

ABSTRACT A reconfigurable antenna with frequency agility and pattern-switching ability is presented in this paper. The proposed design comprises a planar and compact frequency reconfigurable monopole antenna and an active frequency selective surface (AFSS) with reconfigurable transmission and reflection properties in dual bands. The AFSS screen is divided into four sections and each section is composed of four FSS elements with only one PIN diode in each unit cell. The monopole antenna, which is situated at the center of the box-shaped AFSS to illuminate , can reconfigure its resonating frequency between 2.5 and 3.6 GHz with the help of only one PIN diode. With the help of this transformation ability of AFSS behavior electronically, the pattern can be steered across the entire X–Z plane in eight steps at two different operating frequencies independently. This antenna can be very useful in various wireless applications such as drones, small base stations, etc.

AlexNet Classifier and Support Vector Regressor for Scheduling and Power Control in Multimedia Heterogeneous Networks

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively.

Outage analysis for multi-radio heterogeneous networks in the presence of aerial jammers

Recently, aerial jammers (AJs) have been actively considered because of their swiftness and adaptability to maximize the impact of jammers. The previous studies mainly focused on the optimization of a single AJ (transmit power, location, etc.) consisting of intended users and eavesdroppers in single-cell environment. In this paper, macroscopic impact of multiple randomly deployed AJs to multiple radio access technologies (multi-RATs) is analyzed, which have been actively studied for future cellular systems. In particular, multi-radio heterogeneous networks consisting of WiFi-enabled small base stations (SBSs) and 5G-enabled macro/micro base stations (MBSs) are considered. An integrated framework is developed and evaluated for multi-RATs in the presence of both co-channel interference and AJ interference based on tools from stochastic geometry. The main challenge is to analyze the optimal control of portion of AJs to interfere two different RATs to maximize the outage probability. Both analytical and simulation results are presented to demonstrate the impacts of various network parameters to the overall outage probability.

ESticky: An Advanced Remote Reminder System for People with Early Dementia.

While the European population is ageing, the number of people with dementia is dramatically rising. With this comes an increased need for products that help affected people to be more independent and able to live in their own home for as long as possible. eSticky addresses this need by providing a sophisticated reminder system that replaces the old-fashioned sticky notes by electronic versions thereof, which can be programmed from near and far in a device-independent manner via the internet and using a standard web browser. For this purpose, a set of low-cost ePaper displays are used, accompanied by a small and unobtrusive base station situated at the user's home, and supported by a web platform for user management and the authoring and scheduling of the message reminders. The displays can be placed at several strategically useful places in a user's home, to enable users and/or care persons to place reminders that will, based on the user's daily routines, most probably be read. A sub-set of displays, named active displays, even enable the user to press a confirmation button to show that he or she has actually read the reminder. The confirmation is then made available to the authors of the particular reminder through the web platform. The system is developed using a user-centred design approach, to take all stakeholders' wishes and needs into account, in order to come up with a system that is easy to use and provides good service to many people. In this extended version of the paper, besides a detailed description of the technological advancements of the project, both in terms of software and hardware, we provide an insight of the eSticky device design methodology and results from the design user evaluation.

Energy-Efficient Computation Offloading for Static and Dynamic Applications in Hybrid Mobile Edge Cloud System

As a promising paradigm, mobile edge computing (MEC) provides cloud resources in a network edge to offer low-latency services to mobile devices (MDs). MEC addresses the limited resource and energy issues of MDs by deploying edge servers, which are often located in small base stations. It is a big challenge, however, as how to dynamically connect resource-limited MDs to nearby edge servers, and reduce total energy consumption by MDs, small base stations and a cloud data center (CDC) all in a hybrid system. To tackle the challenge, this work provides an intelligent computation offloading method for both static and dynamic applications among entities in such a hybrid system. The minimization problem of total energy consumption is first formulated as a typical mixed integer non-linear program. An improved meta-heuristic optimization algorithm, named <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> article swarm optimization based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> enetic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning (PGL), is tailored to solve the problem. PGL synergistically take advantage of both the fast convergence of particle swarm optimization, and the global search ability of genetic algorithm. It jointly optimizes task offloading of heterogeneous applications, bandwidth allocation of wireless channels, MDs' association with small base stations and/or a cloud datacenter, and computing resource allocation of MDs. Numerical results with real-life system configurations prove that PGL outperforms several state-of-the-art peers in terms of total energy consumption of the hybrid system.

Intelligent Reflecting Surface-Aided Integrated Terrestrial-Satellite Networks

Intelligent reflecting surface (IRS) is a novel technology to manipulate wireless propagation channels via smart and controllable signal reflection. In this paper, we investigate an IRS-aided integrated terrestrial-satellite network (ITSN) system, where the IRS is deployed to assist the co-existing transmissions of the terrestrial small base stations (SBSs) and the satellite. Because of the spectrum sharing in the ITSN, the interference between the two systems should be carefully mitigated. Our objective is to maximize the weighted sum rate (WSR) of all users by jointly optimizing the frame-based coordinated transmit beamforming vectors at the SBSs, the phase shift matrix at the IRS, and the frame user scheduling, subject to SBSs’ individual power constraints and unit modulus constraints of phase shifters. To this end, we first adopt the agglomerative hierarchical clustering (AHC) method to schedule the satellite users to different frames. Then the block coordinate descent (BCD) algorithm is proposed, which alternately optimizes the transmit beamforming vectors and the reflective phase shift matrix. In particular, the optimal transmit beamforming vectors are obtained via the fractional programming (FP) technique. Meanwhile, two efficient algorithms, i.e., the Riemannian manifold (RM) and the successive convex approximation (SCA), are proposed for the phase shift optimization. Finally, simulation results are provided to demonstrate the performance gain of our schemes over other benchmark schemes.

Massive Access in 5G and Beyond Ultra-Dense Networks: An MARL-Based NORA Scheme

Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense Network (UDN) are promising candidates to cope with the massive access challenge of Machine-Type Communications (MTC). The power level pool is crucial for NOMA to bring performance gains. The existing related literatures rarely consider the power level pool design problem, or only resolve it in the single-cell scenario. However, this problem in multi-cell scenario is more complex and difficult to solve due to the presence of inter-cell interference. Towards this end, we propose a Non-Orthogonal Random Access (NORA) scheme to enable the coexistence of Human-Type Communications (HTC) and MTC for 5G and beyond UDN, where the power level pool design problem in multi-cell scenario is our focus. In order to deal with the complexity caused by multiple optimization objectives and inter-cell interference, we present a Multi-Agent Reinforcement Learning (MARL)-based solution to solve this problem, where each small base station acts as an agent to learn a suitable gap between adjacent power levels. Extensive numerical comparisons demonstrate the superior performances of our proposed scheme in multiple perspectives.

Sum-Rate Systematic Intercell Interference Coordination Techniques for5GHeterogeneous Networks

In the next generation of wireless networks, small base stations are being adopted to increase the system's speed and capacity. However, this adoption increases the likelihood of inter-cell interference, which can degrade performance the system. This report discusses different approaches to managing interference that can mitigate these effects. Currently, a few techniques are already in use in 4G systems, while others have been suggested but not put into practice. The purpose of this study is to conduct an analytical examination of the different and various interference management methods that could be utilized in Heterogeneous Networks (HetNets), focusing specifically on HetNets with high density of small cells as they are expected to be part of the network topology for 5G networks and beyond. The study involved implementing time-based enhanced inter-cell interference coordination (eICIC) by establishing coordination between macro and small base stations. The results showed that robust communication with low latency is essential, and backhaul connections play a critical role in achieving this. Consequently, the network's sum-rate was increased.

An efficient artificial rabbits optimization‐based user association problems in ultradense network using deep learning model

SummaryThe ability of high splitting gain of dense small cells contributed to the rapid establishment of ultradense networks (UDNs). Its higher efficiency to deal with high traffic data demand made UDN a most‐promising technology for the future 5G environment. However, the UDN creates concern about user association, which causes more complexities in providing a high data transmission rate and low latency rate. To tackle these complexities, in this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods. The performance evaluation is carried out with regard to network lifetime, end‐to‐end delay, packet delivery ratio, sum rate overall energy consumption, latency, and minimum rate and maximum rate of the network. The proposed AEMDPG‐ARO algorithm gives better performance with reduced time complexity and better metrics rate in the result analysis. The minimum latency achieved by the proposed AEMDPG‐ARO algorithm is about 0.1 s.

Energy and Latency Efficient Caching in Mobile Edge Networks: Survey, Solutions, and Challenges

Future wireless networks provide research challenges with many fold increase of smart devices and the exponential growth in mobile data traffic. The advent of highly computational and real-time applications cause huge expansion in traffic volume. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station introduces the use of caches at the edge of the networks. Storing most popular files at the edge of mobile edge networks (MENs) in user terminals (UTs) and small base stations caches is a promising approach to the challenges that face data-rich wireless networks. Caching at the mobile UT allows to obtain requested contents directly from its nearby UTs caches through the device-to-device (D2D) communication. In this survey article, solutions for mobile edge computing and caching challenges in terms of energy and latency are presented. Caching in MENs and comparisons between different caching techniques in MENs are presented. An illustration of the research in cache development for wireless networks that apply intelligent and learning techniques in a specific domain in their design is presented. We summarize the challenges that face the design of caching system in MENs. Finally, some future research directions are discussed for the development of cache placement and cache access and delivery in MENs.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

Experimental studies of communication channel bandwidth of millimeter wave range indoors

There is a rapid development of technologies worldwide that have access to the Internet, independently exchange data, and upload collected information to cloud storage. Every year, the amount of data transmitted from things and people on the Internet is approximately two zettabytes. The volume of data on the Internet is constantly growing. According to the Internet Live Stats service, more than 50,000 search queries are made on Google every second, 120,000 videos are viewed on YouTube, and almost 2.5 million e-mails are sent. Therefore, it is expedient to implement the fifth generation of communication, which will work in the millimeter range. Promising technologies for building 5G and its implementation include small base stations, which are analogs of conventional mobile network base stations. Within settlements, small base stations should be located short distances from each other (approximately 250 meters). From such parameters, it can be concluded that it will be necessary to use thousands of such stations to cover even a small city, forming a single network that will transmit data to 5G users. Among the advantages, it can be noted that due to the small size of the antennas, they can be placed on any elements of structures and buildings (for example, on the walls of buildings, pillars, etc.) without additional obstacles. The paper presents and investigates the bandwidth of the millimeter wave range indoor communication channel. The structure of the modem and the format of the control command, the dependence of the transmission speed C on the length of the radio line of the IEEE 802.11ad 60 GHz communication system in point-to-point mode (Indoor LOS) and (Indoor NLOS) are presented. In work, there are theoretical assessments of the achievable data transfer rates based on the bandwidth of the Shannon communication channel and practically possible data transfer rates for various segments of the information transfer network for the IEEE 802.11ad standard equipment using different modulation and coding schemes (MCS).

Echo State Network-Based Content Prediction for Mobile Edge Caching Networks

With the rapid development of internet communication and the wide application of intelligent terminal, moving the cache to the edge of the network is an effective solution to shorten the delay of users accessing content. However, the existing cache work lacks the comprehensive consideration of users and content, resulting in low cache hit ratio and low accuracy of the whole system. In this paper, the authors propose a collaborative caching model that considers both user request content and content prediction, so as to improve the caching performance of the whole network. Firstly, the model uses the clustering algorithm based on Akike information criterion to cluster users. Then, combined with the clustering results, echo state network is used as the machine learning framework to predict the content. Finally, the cache contents are selected according to the prediction results and cached in the cache unit of the small base station. Simulation results show that compared with the existing cache algorithms, the proposed method has obvious improvement in cache hit ratio, accuracy, and recall rate.

Resource Management and Reflection Optimization for Intelligent Reflecting Surface Assisted Multi-Access Edge Computing Using Deep Reinforcement Learning

Multi-access edge computing (MEC) enables the computation-intensive and latency-critical application to be processed at the network edge, which reduces the transmission latency and energy consumption. The quality of the wireless channel seriously affects the performance of the edge network. Consequently, the performance of the edge network can be significantly improved from the perspective of communication. The recently advocated intelligent reflecting surface (IRS) intelligently controls the radio propagation environment to improve the quality of wireless communication links. This paper proposes an edge heterogeneous network with the assistance of intelligent reflecting surface. Specifically, the macro base station and small base stations are equipped with MEC servers, and IRS is adopted to provide an additional computation offloading link. The user association, computation offloading and resource allocation, as well as IRS phase shift design are optimized with the aim of minimizing the long-term energy consumption subject to the constraints imposed on quality of service (QoS) and available resources. The challenge of the optimization problem is rooted from the fact that update timescale of user association is different from others. Hence, a two-timescale mechanism is invoked by marrying tools from matching theory and deep reinforcement learning. More specifically, the user association decision takes place in the long timescale. In the short timescale, the computation offloading, resource allocation and IRS phase shift design strategy is performed. The effectiveness of the proposed two-timescale mechanism is verified by the simulation results.

Recursive ESPRIT Algorithm for Multi-User OAM Low-Overhead AoA Estimation

Multi-user orbital angular momentum (MU-OAM) wireless backhaul systems require precise alignment of transmitted and received OAM beams in line-of-sight (LoS) scenarios. Therefore, a crucial feature for a macro base station (MBS) is to accurately obtain the angle of arrival (AoA) of OAM beams from the small base stations (SBSs) in the system. Considering the limitations of existing OAM-based AoA estimations, we propose a novel super-resolution AoA estimation method, denoted as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recursive estimation of signal parameters via rotational invariance techniques (ESPRIT)</i> method. The proposed technique is fundamentally different from traditional ESPRIT algorithm: It is not based on the displacement-invariant array structure, but on the recursive characteristic of Bessel functions in OAM signals. With this method, the inter-mode interference of the LoS MU-OAM system can be significantly suppressed by less training overhead, and the performance is close to that of the ideal system.

On the application of uplink/downlink decoupled access in heterogeneous mobile edge computing

Mobile edge computing (MEC) is a key player in low latency 5G networks with the task to resolve the conflict between computationally-intensive mobile applications and resource-limited mobile devices (MDs). As such, there has been intense interest in this topic, especially in multi-user single-server and homogeneous multi-server scenarios. However, the research in the heterogeneous multi-server scenario is limited, where the servers are located at small base-stations (SBSs), macro base-stations (MBSs), or the cloud with different computing and communication capabilities. On the other hand, computational-tasks offloading is limited by the type of MD-BS association with almost all previous works focusing on offloading the MD’s computational tasks to the MEC servers/cloudlets at its serving BS. However, in multi-BS association, or downlink/uplink decoupled (DUDe) scenarios, an MD can be served by multiple BSs and hence has multiple offloading choices. Motivated by this, we proposed a joint BS association and subchannel allocation algorithm based on a student-project allocation (SPA) matching approach to minimize the network sum-latency, which breaks the constraint that one MD must connect to the same BS in the UL and DL, and jointly consider the communication and computational disparity of SBS and MBS cloudlets in heterogeneous MEC networks. Moreover, an optimal power allocation scheme is proposed to optimize the system performance subject to the predefined quality of service constraints. Our results show that the proposed scheme is superior to benchmark techniques in enabling the effective use of computational and communication resources in heterogeneous MEC networks.

Mode Selection and Resource Allocation for SWIPT NOMA based Three-tier HetNets

In next-generation heterogeneous networks, device-to-device communications are a key enabler to support a wider and diverse range of applications. They play an important role to guarantee the performance and quality of service requirements for the future ultra-dense networks. D2D-enabled cellular networks allow user equipment to communicate directly without any or with a partial association with macro base stations or with small base stations. In this paper, we propose a distance-based dynamic approach to minimize the interference and to guarantee QoS for diverse types of users equipment, such as macro user, small user and D2D user. The mode selection is used for minimize the interference and to guarantee QoS for both cellular and D2D communication links. Moreover, we investigated hybrid simultaneous wireless information and power transfer to achieve the higher data rates while guaranteeing the minimum harvested energy. Additionally, our scheme employs a transmit antenna selection criterion to exploit the spatial diversity gain of a multiple antennas for small base station. Results obtained show that coverage probability is improved in macro and small base stations links, when compared with traditional neighbor-based methods, we validate the theoretical findings and demonstrate that significant performance gain over orthogonal multiple access scheme in terms of spectral efficiency can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system where we show the energy efficiency.