Throughput Analysis of LTE-Licensed-Assisted Access Networks with Imperfect Spectrum Sensing

Fu, Zhuoran; Xu, Wenjun; Zhang, Feng; Lin, Xuehong; Lin, Jiaru

doi:10.1109/wcnc.2017.7925651

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…In order to avoid the performance degradation caused by the systems coexistence, and guarantee a fairness between WLAN systems and LTE networks, it is necessary to design a channel access mechanism for LTE-LAA systems [62]. In the text followed, several research works dealing with the fairness issue toward Wi-Fi networks will be introduced.…”

Section: Coexistence Mechanismsmentioning

confidence: 99%

“…[61] proposed an analytical model to evaluate the performance of an LBT based coexistence scheme with multiple LTE SBSs. [62] analyzed and optimized the throughput performance of LTE-LAA networks coexisting with WLAN under imperfect spectrum sensing.  LBT with TXOP backoff: There are two more configurable parameters of carrier sense multiple access with collision avoidance (CSMA/CA) for different QoS provisions, including (1) arbitration inter-frame spaces (AIFS), and (2) transmission opportunity (TXOP).…”

Section:  Frame Based Equipment (Fbe) and Load Based Equipment (Lbe): According To Technical Reports Written By European Telecommunicatimentioning

confidence: 99%

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Applications of Intelligent Radio Technologies in Unlicensed Cellular Networks - A Survey

Huang¹,

Chen²

2021

KSII TIIS

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Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

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Section: Coexistence Mechanismsmentioning

confidence: 99%

Section:  Frame Based Equipment (Fbe) and Load Based Equipment (Lbe): According To Technical Reports Written By European Telecommunicatimentioning

confidence: 99%

Applications of Intelligent Radio Technologies in Unlicensed Cellular Networks - A Survey

Huang¹,

Chen²

2021

KSII TIIS

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“…where γ ∈ [0, 1] denotes the discounted rate. r l (t + 1) is defined in (12). The training method for all DQNs is shown as Algorithm 1.…”

Section: Dqn-based Spectrum Access Strategymentioning

confidence: 99%

Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach

Chang

Song

et al. 2019

IEEE Internet Things J.

181

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Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination with other secondary users. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network (RNN), called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RCbased spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.Index Terms-Dynamic spectrum access (DSA), deep reinforcement learning (DRL), deep Q-network (DQN), reservoir computing (RC), echo state network (ESN), and resource allocation.

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“…In [13], the expected network throughput of the CLWNets considering the frame structure and the back-off time was analysed and validated by Monte Carlo simulation. In [14], the throughput of the LTE-LAA networks with imperfect spectrum sensing was analysed based on the discrete-time Markov chain model. The results confirmed that the throughput was significantly affected by the imperfect spectrum sensing, and the throughput can be improved by jointly optimizing the sensing duration and sensing threshold.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

“…, can be approximately obtained as the expression in (14), as shown at the bottom of the next page. For the WUE, its Palm coverage probability p y 0 (r W 0 , λ W , λ L , T W , M ) can be approximately obtained as the expression in (15), as shown at the bottom of the next page conditioned on the serving WAP y 0 located at (r W 0 , 0).…”

Section: B the Palm Coverage Probabilitymentioning

confidence: 99%

Density Analysis of LTE-LAA Networks Coexisting With WiFi Sharing Multiple Unlicensed Channels

Gao

Zhang

et al. 2019

IEEE Access

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With data traffic explosion, operating Long-Term Evolution (LTE) in the 5 GHz unlicensed band, which has already been used by WiFi networks, has been proposed. To harmoniously coexist with the incumbent WiFi networks, LTE-Licensed Assisted Access (LAA) has been proposed recently, advocating cellular networks to access the unlicensed band by employing listen-before-talk mechanism. However, the performance of LAA has not been analysed under multiple accessible unlicensed channels (UCs). In this work, we analyse the user throughput and spatial spectral efficiency (SSE) of the multi-UC coexisting LTE-LAA and WiFi networks versus the network density based on the Matern hard core process. The throughput and SSE are obtained as functions of the downlink successful transmission probability (STP), of which analytical expressions are derived and validated by Monte Carlo simulations. The results show that an optimal LTE access point (LAP) density exists to maximise the LTE-LAA user equipment (LUE) throughput, and our derived closed-form STP lower bound of LUE can be used to obtain a sufficiently accurate prediction of the optimal LAP density. Moreover, the SSE does not change much under relatively low LAP densities, and when the LAP density is larger than 1, 585 LAPs per km 2 , the SSE approaches the asymptotic SSE as the LAP density approaches infinity. INDEX TERMS LTE-LAA, WiFi, multiple unlicensed channels, Matern hard core process, successful transmission probability.

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Throughput Analysis of LTE-Licensed-Assisted Access Networks with Imperfect Spectrum Sensing

Cited by 7 publications

References 13 publications

Applications of Intelligent Radio Technologies in Unlicensed Cellular Networks - A Survey

Applications of Intelligent Radio Technologies in Unlicensed Cellular Networks - A Survey

Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach

Density Analysis of LTE-LAA Networks Coexisting With WiFi Sharing Multiple Unlicensed Channels

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