Licensed Assisted Access Research Articles

BANDWIDTH AND GAIN ENHANCEMENT OF CUP SHAPED UWB MICROSTRIP PATCH ANTENNA WITH DEFECTIVE GROUND PLANE FOR SUB-6 GHZ 5G APPLICATIONS

This paper presents the design of slotted ground plane and a circular ring slotted microstrip patch antenna for new radio (NR) band, sub-6 GHz, WLAN and Licensed Assisted Access (LAA) 5G applications. The design consists of cup -shaped microstrip patch antenna with a defective ground structure (DGS). The circular ring slot at the top of the patch and the modified rectangular slots at the bottom are helpful to improve the performance of the antenna, thereby increase the efficiency, bandwidth, gain and improved s-parameters. The proposed antenna is designed on FR4-epoxy substrate and inset feeding technique. The design and simulation of my proposed work are done using HFSS (ANSYS-Electronics suite 19.2×64 version). The antenna has a bandwidth of 2.56 GHz with maximum gain of 2.27 dB. The antenna operates for the bandwidth from 4.04GHz to 6.6 GHz which covers NR-New Radio band (3.4-4.2 GHz), Sub 6 GHz 5G band (n-79) (4.44-5.0 GHz), WLAN (5.15 - 5.35 GHz) and Licensed Assisted Access (LAA) band.

A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario.

Recent studies and literature reviews have shown promising results for 3GPP system solutions in unlicensed bands when coexisting with Wi-Fi, either by using the duty cycle (DC) approach or licensed-assisted access (LAA). However, it is widely known that general performance in these coexistence scenarios is dependent on traffic and how the duty cycle is adjusted. Most DC solutions configure their parameters statically, which can result in performance losses when the scenario experiences changes on the offered data. In our previous works, we demonstrated that reinforcement learning (RL) techniques can be used to adjust DC parameters. We showed that a Q-learning (QL) solution that adapts the LTE DC ratio to the transmitted data rate can maximize the Wi-Fi/LTE-Unlicensed (LTE-U) aggregated throughput. In this paper, we extend our previous solution by implementing a simpler and more efficient algorithm based on multiarmed bandit (MAB) theory. We evaluate its performance and compare it with the previous one in different traffic scenarios. The results demonstrate that our new solution offers improved balance in throughput, providing similar results for LTE and Wi-Fi, while still showing a substantial system gain. Moreover, in one of the scenarios, our solution outperforms the previous approach by 6% in system throughput. In terms of user throughput, it achieves more than 100% gain for the users at the 10th percentile of performance, while the old solution only achieves a 10% gain.

Fair and Efficient Spectrum Sharing in Unlicensed Bands: Does Number of Links Matter?

The rapid growth of mobile data traffic has become a great challenge to the licensed spectrum in scarcity. Driven by this, deploying networks in unlicensed bands has been drawing significant attention. This work is devoted to study the impact of the number of links on optimal coexistence performance between Licensed-Assisted Access (LAA) and WiFi in unlicensed bands. In particular, we consider three coexistence cases, including 1) single LAA link coexisting with single WiFi link; 2) single LAA link coexisting with multiple WiFi links; 3) multiple LAA links coexisting with multiple WiFi links. The throughput performance in all the three cases is characterized as explicit expressions, based on which the maximum total throughput under the throughput fairness and the corresponding optimal initial backoff window sizes are derived. A systematic comparison among the optimal performance of the three cases is conducted, and it is found that while the maximum total throughput is insensitive to the change of the number of multiple links, the drastic change from single link to multiple links in one network would lead to fierce internal competition in that network, and thus deteriorates both the optimal throughputs of the coexisting network and WiFi. This work sheds important light on the realization of fair and efficient spectrum sharing.

Intelligent Access to Unlicensed Spectrum: A Mean Field Based Deep Reinforcement Learning Approach

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

Spectrum Sharing Schemes From 4G to 5G and Beyond: Protocol Flow, Regulation, Ecosystem, Economic

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime—that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecommunication vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors.

On the Effects of Transmit Power Control on Multi Carrier LAA-WiFi Coexistence

To meet the ever increasing data rate demand in cellular network, the 3rd Generation Partnership Project (3GPP) introduced Licensed Assisted Access (LAA) with Listen Before Talk (LBT) mechanism to utilize the unlicensed spectrum to supplement the licensed LTE users. However, this leads to interference to already incumbent technologies in unlicensed spectrum such as WiFi. Furthermore, multi-carrier aggregation has been proposed to further increase the capacity in unlicensed spectrum. As WiFi primarily works on the principle of energy detection in the channel, in this paper, we first develop an analytical model to measure the performance of LAA and WiFi coexistence in unlicensed spectrum which supports multi-carrier aggregation which employs SINR based collision that allows the possibility of concurrent transmissions. Further, to reduce the interference to WiFi, we propose a coexistence method named Carrier Aggregation with Transmit Power Control (CATPC) which dynamically adjusts the LAA transmit power. CATPC provides a better opportunity for WiFi devices, better channel utilization, and energy efficient transmission by LAA. Through extensive simulations we demonstrate the effectiveness of CATPC when compared to other coexistence methods proposed in the literature.

Effective capacity optimization for UDN

For the ultra-dense distributed network (UDN), the intensive deployment leads to the collision problem of the unlicensed spectrum access due to the random contention access mechanism of the unlicensed spectrum, which is difficult to improve the user quality of service (QoS). Here, the wireless access process of the licensed assisted access (LAA) network is modelled as a four-state semi-Markov model. Then, the effective capacity (EC) of the UDN based on the random access is derived, and the network capacity limit under the QoS requirements in the unlicensed spectrum is given. Based on the obtained EC, two power control algorithms are proposed to maximize the EC and effective energy efficiency (EEE) respectively under the user QoS requirements. Finally, numerical results verify the accuracy of the proposed theory, and the capacity-delay domain of the UDN is given. The simulation results show that the proposed algorithms improve the EC and the EEE compared with existing classical schemes.

Bi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing

Radio spectrum is a scarce resource. To meet demands, new wireless technologies must operate in shared spectrum over unlicensed bands (coexist). We consider coexistence of Long-Term Evolution (LTE) License-Assisted Access (LAA) with incumbent Wi-Fi systems. Our scenario consists of multiple LAA and Wi-Fi links sharing an unlicensed band; we aim to simultaneously optimize performance of both coexistence systems. To do this, we present a technique to continuously estimate the Pareto frontier of parameter sets (traces) which approximately maximize all convex combinations of network throughputs over network parameters. We use a dimensionality reduction approach known as active subspaces to determine that this near-optimal parameter set is primarily composed of two physically relevant parameters. A choice of two-dimensional subspace enables visualizations augmenting explainability and the reduced-dimension convex problem results in approximations which dominate random grid search.

Machine Learning for Hidden Nodes Detection in Unlicensed LTE Networks

This paper analyzes and evaluates the use of Machine Learning (ML) techniques as an alternative to heuristic methods to identify UEs in Licensed-Assisted Access (LAA) networks that are being affected by collisions from hidden nodes sources. The paper analyzes different models based on supervised ML methods to classify UEs into two possible classes: UEs affected by collisions and UEs free of collisions. To implement them, we employ a set of typical parameters of the LTE standard, e.g., Channel Quality Indicator (CQI), Reference Signal Received Quality (RSRQ) and number of Physical Resource Blocks (PRB). These parameters are fed into different supervised ML models which, once optimized, obtain the best models to predict when an UE is facing interference from hidden nodes in a dynamic channel environment. Results show that classification methods based on ML techniques are capable of predicting with a high level of accuracy when a UE is affected by collisions and, consequently, if this node is located in a hidden area. Nevertheless, and despite the growing interest in applying ML techniques in an increasing number of fields, we can conclude that, in this case, the performance of the ML approach is not necessarily better than heuristic methods. For instance, it does not outperform the Dynamic Collision Detection (DCD) previously presented by the authors. In addition, the ML algorithms exhibit a higher prediction time and computational complexity, with respect to those obtained with DCD algorithm. This should be considered a drawback in some LTE-Advanced and 5G wireless network scenarios that prioritize lower latencies.

Identification and Analysis of a Unique Cell Selection Phenomenon in Public Unlicensed Cellular Networks Through Machine Learning

Cellular operators deploy 4G License Assisted Access (LAA) and 5G NR-U base stations in the unlicensed spectrum, to enhance the overall network capacity. This work highlights a unique Physical Cell Id (PCI) related phenomenon observed in the public LAA operator deployments. Notably, the licensed and unlicensed carriers of a device may have the same PCI or different PCIs. The phenomenon is triggered by the combined effect of unlicensed deployment architectures and cell selection mechanisms. Consequently, the phenomenon will intensify manifold in 5G NR-U whose public deployment will soon commence. Unfortunately, the impact of this phenomenon on coexistence network performance is unexplored. It is also desirable to accurately identify the PCI scenarios at the device for improved cell selection and network performance. However, the data imbalance makes the classification problem challenging. This work addresses these problems through the following approach. Operator data from three LAA cellular providers is gathered and analyzed using machine learning algorithms. The impact of the phenomenon on LTE, LAA, and Wi-Fi components is demonstrated in three steps: First, the variation in network performance prediction accuracy in the PCI scenarios is examined. Second, the efficacy of numerosity reduction techniques used in data-driven cell selection is evaluated in both PCI scenarios. The third step entails a comparison of operator data analysis with network measurements. On-site experiments are conducted at the same PCI and different PCI sites to study the differences in real-time network performance. A controlled LTE-WiFi coexistence environment is created and multiple traffic categories are considered. Finally, a class-weight-based solution is proposed for PCI scenario identification. F-score of 0.75 and AUC-ROC of 0.84 is achieved for LAA, with a minimalist feature-set of SINR and Throughput.

3GPP V2X on Unlicensed Spectrum: Performance Analysis and Optimum Channel Access Strategies

Featuring unprecedentedly high data rates and high connection densities, 3GPP New Radio (NR) vehicle-to-everything (V2X) communications have been regarded as the most promising remedy to support the next generation driving use cases. For this purpose, although dedicated bands on 5.9 GHz spectrum have been allocated for Intelligent Transportation Systems (ITS), the limited bandwidth may not afford the desired service requirements. This engineering concern thus drives the utilization of unlicensed bands on 5 GHz spectrum adjacent to the ITS bands. However, subject to the fairness constraint in sharing unlicensed spectrum, various communication regulations have been imposed, and four different types of multi-band channel access procedures are consequently developed both in 3GPP LTE-A Licensed-Assisted Access (LAA) and NR on unlicensed spectrum (NR-U), to lead to a phenomenon that occupying more unlicensed bands may not suggest a proportional growth in the throughput. To empower high-throughout and high-density V2X communications, in this paper, we consequently propose the optimum channel access strategies for each V2X transmitter to decide the optimum number of unlicensed bands to access. To this end, analytic models for four types of multi-band channel access procedures in 3GPP NR-U are provided, which capture both inter-system and intra-system interference from coexisting WiFi transmitters and V2X transmitters. Subsequently, game-theoretically optimum strategies for each type of multi-band channel access procedures are derived. Performance evaluation results fully demonstrate the optimality of the proposed strategies, to serve the urgent demands in supporting the next generation driving use cases on unlicensed spectrum.

A Q-Learning Based Energy Threshold Optimization Algorithm in LAA Networks

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput.

Enhanced network sensitive access control scheme for LTE–LAA/WiFi coexistence: Modeling and performance analysis

Implementation of 5G and beyond networks is looking to expand the operation of licensed systems into unlicensed frequency bands through technologies such as license assisted access (LAA) and New Radio Unlicensed (NR-U). LAA aggregates licensed and unlicensed bands for long term evolution (LTE) implementation to address the ever-increasing demand for cellular data traffic and shortage of licensed spectrum. Spectrum-efficient resource-sharing schemes are, however, critical for the harmonious coexistence of LTE–LAA with incumbent systems on the unlicensed band, especially WiFi, while opportunistically improving LTE throughput and enhancing spectrum utilization of the unlicensed band. In this paper, we present a listen before talk (LBT) based clear channel assessment (CCA) mechanism for LAA eNodeBs (eNBs) to improve the coexistence performance of LTE–LAA with WiFi. Particularly, we propose an adaptive exponential backoff scheme for LTE eNB that dynamically updates the contention window (CW) size and transmission opportunity (TXOP) parameters according to network load variations. A three-dimensional discrete Markov model is developed to describe the LBT procedure of LAA eNB, and a performance model is further derived to evaluate steady-state channel access, transmission, and failure probabilities. The proposed scheme’s performance is evaluated in terms of successful transmission probability, throughput, and delay according to the 3GPP and WiFi guidelines. The results are compared with the traditional schemes proposed in literature considering fixed and adaptive CW size for LAA eNB.

Joint trajectory and transmission optimization for energy efficient UAV enabled eLAA network

Unmanned aerial vehicle (UAV) as a carrier of small cell base station (SBS) becomes a promising solution to reduce infrastructure deployment cost. At the same time, Licensed-assisted Access (LAA) is proposed by 3GPP to apply the Long Term Evolution (LTE) technology into the unlicensed spectrum to expand the available bandwidth. To enhance and supplement the next generation 5G wireless network, this paper integrates LAA technology into UAV network to provide communication service for high-density mobile users. An enhanced LAA (eLAA) network is adopted to coordinate unlicensed band for LTE and WiFi by leveraging IEEE 802.11e protocol. The limited on-board energy is the main challenge for UAV to support mobility and communication. Under the UAV enabled eLAA network, we propose a joint trajectory design and transmission optimization algorithm to maximize the energy efficiency (EE) of the UAV. Firstly, for single UAV network, the formulated nonlinear fractional problem is solved by block coordinate descent (BCD) method and Dinkelbach-type algorithm. Our algorithm is proved to converge to a stationary point of the original nonconvex problem. Then, the proposed algorithm is extended to multi-UAV network through the sum-of-ratios algorithm. Numerical results demonstrate that the EE performance is significantly improved by UAV mobility. Based on the optimal trajectories, frequency reuse among multiple UAVs can further improve the EE performance.

Performance-Fairness Trade-off for Wi-Fi and LTE-LAA Coexistence

Long Term Evolution (LTE)-License Assisted Access (LAA), which leverages unlicensed resource sharing with the Wi-Fi network, is a promising technique to address the spectrum scarcity issue in present and future wireless networks. However, unlicensed spectrum sharing between Wi-Fi and LAA requires fair resource allocation with specific performance guarantees for both sets of Wi-Fi and LAA stations. In this paper, an optimal communication policy is devised for LAA stations coexisting on a single unlicensed channel with Wi-Fi stations. The inter-network collisions are avoided through non-overlapping transmission phases for Wi-Fi and LAA networks. The throughput performance of LAA network is maximized while guaranteeing a proportionally fair performance among LAA stations and a fair share for Wi-Fi stations. The proposed scheme, unlike the state-of-the-art coexisting mechanism, jointly optimizes the transmission probability and the transmission rate for each LAA station. The formulated optimization problem to maximize network throughput is solved analytically. The numerical results demonstrate a significant improvement in the LAA throughput, more than 75%, as compared to the case when transmission probabilities are not optimized. Moreover, a notable gain of 8-9% in the fairness index reflects the intra-network fairness of the proposed LAA network over the conventional LAA network.

Listen Before Receive (LBR) Assisted Network Access in LAA and WiFi Heterogeneous Networks

The Listen-Before-Talk (LBT) is the main procedure for Licensed Assisted Access (LAA) to accomplish fair and friendly coexistence with other operators or technologies operating over unlicensed spectrum. However, in LBT, the lack of coordination with other existing systems brings challenges in sustaining performance in the coexistence of LAA and WiFi networks. Specifically, the hidden node problem (HNP) and exposed node problem (ENP) cannot be effectively handled when both LAA and WiFi nodes attempt to access the unlicensed spectrum at the same time. Thus, transmission failure might occur and the network performance would be degraded. In order to mitigate the influences caused by HNP and ENP, based on LBT, we firstly analyze HNP and ENP by means of mathematical approach. The analytical results surprisingly reveal that the hidden node and exposed node probabilities are as high as 41% and 39.33%, respectively. Then, a Give And p-persistent Take (GAT) mechanism with the Listen-Before-Receive (LBR) procedure, namely LBR-GAT, is proposed to cope with LBT to reduce the collision caused by HNP as well as to retrieve the bandwidth sacrificed by the ENP. With LBR-GAT, the LAA sender conditionally gives up or takes back transmission opportunities, and thus the unlicensed spectrum could be efficiently shared between LAA and WiFi. Evaluation results show that the proposed LBR-GAT could conditionally obtain better network performance comparing to legacy LBT.

Coordinated Allocation of Radio Resources to Wi-Fi and Cellular Technologies in Shared Unlicensed Frequencies

Wireless connectivity is essential for industrial production processes and workflow management. Moreover, the connectivity requirements of industrial devices, which are usually long-term investments, are diverse and require different radio interfaces. In this regard, the 3GPP has studied how to support heterogeneous radio access technologies (RATs) such as Wi-Fi and unlicensed cellular technologies in 5G core networks. In some cases, these technologies coexist in the same spectrum. Dynamic spectrum sharing (DSS), which has already been proven to increase spectrum efficiency in licensed bands, can also be applied to this scenario. In this paper, we propose two solutions for mobile network operators (MNOs) or service providers to dynamically divide (multiplex) the radio resources of a shared channel between a Wi-Fi basic service set (BSS) and one or several carriers of scheduled wireless networks, such as cellular technologies, with a configurable level of sharing granularity. These solutions do not require modifications to the current commercial off-the-shelf (COTS) end devices. We adapt the existing IEEE 802.11 procedures to notify the Wi-Fi stations that they must share channels with different access networks. We demonstrate that our dynamic sharing proposals are also advantageous over direct coexistence and evaluate each of them quantitatively and qualitatively to determine when one or the other is preferable. The evaluation is particularized for IEEE 802.11ac and long-term evolution (LTE) license assisted access (LAA), but the solutions can be easily extended to 5G new radio-unlicensed (5G NR-U) or to any other wireless technology in which the network side schedules end device transmissions.

Coexistence of Cellular and IEEE 802.11 Technologies in Unlicensed Spectrum Bands -A Survey

This paper provides a comprehensive survey on the coexistence of cellular and IEEE 802.11 standards from a holistic viewpoint that takes into account the coexistence of all existing and future cellular and IEEE 802.11 standards in all the available unlicensed spectrum bands. Unlike existing survey works focusing mostly on any unlicensed band and/or standard, we start by giving an overview of unlicensed spectrum bands, including 2.4 GHz, 5 GHz, 6 GHz, and 60 GHz. We then review the operation of cellular technologies, namely Long-Term Evolution Unlicensed (LTE-U), Licensed Assisted Access (LAA), and New Radio Unlicensed (NR-U), worldwide in the unlicensed spectrum bands. Further, we summarize scenarios and categories of coexistence mechanisms, conditions for a fair coexistence, and coexistence-related features. An extensive study on the coexistence mechanisms, deployment scenarios, as well as standardization efforts for the coexistence between cellular and IEEE 802.11 standards, is carried out. Finally, we highlight the coexistence challenges and open problems, the convergence of the Third Generation Partnership Project (3GPP) and IEEE standards, as well as future research directions. Moreover, to provide insights on the relative measures, we also carry out comparative studies of several key concerns with regard to the coexistence, namely unlicensed spectrum band, regulatory requirement, coexistence mechanism, and cellular standardization effort. Each study presents a comparison among potential features of one of these concerns in tabular forms. Finally, we summarize key lessons that are learned and discussed throughout the paper.

Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

The paper investigates statistical distribution testing-based detection methods in an intermittent signal detection scenario. The relevance of the research is driven by 5G networks based on packet transmission, incorporating the concept of cognitive radio and adapting spectrum detection methods from Long Term Evolution (LTE) Licensed-Assisted Access (LAA). The conducted study refers to the recently proposed methods based on testing goodness-of-fit (GoF) of statistical distributions, which are compared with a conventional energy detector. The authors examine the applicability of well-known GoF methods in intermittent transmission, as they require reconsideration in 5G communication systems, and investigate the behavior of the innovative energy-based GoF. The experiments are carried out for different transmitter activity factors, i.e., channel occupancy and signal-to-noise ratio (SNR), demonstrating the superiority of the GoF-based methods in general and particularly the invented GoF test over other energy-based detectors for discontinuous signals detection.

Wireless Coexistence of Cellular LBT Systems and BLE 5.

The 2.4 GHz spectrum is home to several Radio Access Technologies (RATs), including ZigBee, Bluetooth Low Energy (BLE), and Wi-Fi. Accordingly, the technologies’ spectrum-sharing qualities have been extensively studied in literature. License-Assisted Access (LAA) Listen-Before-Talk (LBT) has been identified in technical reports as the foundation for the channel access mechanism for 5G New Radio-Unlicensed (NR-U) operating in the 2.4 GHz Industrial, Scientific, and Medical (ISM) band. The introduction of NR-U into this band raises new concerns regarding coexistence of the newcomer with traditional incumbents. This article reports an investigation of BLE 5 and cellular LBT coexisting systems by means of empirical evaluation. The importance of this study stems from that the studied LBT mechanism is indicative of how 5G NR-U would perform in the 2.4 GHz band. Tests were performed in conformity with the American National Standards Institute (ANSI) C63.27 standard for evaluation of wireless coexistence, and results were reported in terms of throughput and interframe delays. In accordance with the standard and under different BLE physical layers (PHYs) and LBT priority classes, three setups were investigated. These pertain to the three tiers of evaluation, which correspond to the criticality of the device under test. Results demonstrated how BLE throughput dropped as the intended-to-unintended signal ratio decreased, and LBT classes exhibited a diminishing effect as the class priority descended. Long Range BLE PHY was found to sustain longer gap times (i.e., delay) than the other two PHYs; however, it showed less susceptibility to interference. Results also demonstrated that low data rate BLE PHYs hindered the LBT throughput performance since they correspond to longer airtime durations.