A Q-learning scheme for fair coexistence between LTE and Wi-Fi in unlicensed spectrum

During the last years, the growth of wireless traffic pushed the wireless community to search for solutions that can assist in a more efficient management of the spectrum. Toward this direction, the operation of long term evolution (LTE) in unlicensed spectrum (LTE-U) has been proposed. Targeting a global solution that respects the regional regulations worldwide, 3GPP has published the LTE licensed assisted access (LAA) standard. According to LTE LAA, a listen before talk (LBT) procedure must precede any LTE transmission burst in the unlicensed spectrum. However, the proposed standard may cause coexistence issues between LTE and Wi-Fi, especially in the case that the latter does not use frame aggregation. Toward the provision of a balanced channel access, we have proposed mLTE-U that is an adaptive LTE LBT scheme. According to mLTE-U, LTE uses a variable transmission opportunity (TXOP), followed by a variable muting period. This muting period can be exploited by co-located Wi-Fi networks to gain access to the medium. In this paper, the system model of the mLTE-U scheme in coexistence with Wi-Fi is studied. In addition, mLTE-U is enhanced with a Q-learning technique that is used for autonomous selection of the appropriate combinations of TXOP and muting period that can provide fair coexistence between co-located mLTE-U and Wi-Fi networks. Simulation results showcase the performance of the proposed model and reveal the benefit of using Q-learning for self-adaptation of mLTE-U to the changes of the dynamic wireless environment, toward fair coexistence with Wi-Fi. Finally, the Q-learning mechanism is compared with conventional selection schemes showing the superior performance of the proposed model over less complex mechanisms

An adaptive LTE listen-before-talk scheme towards a fair coexistence with Wi-Fi in unlicensed spectrum

The technological growth combined with the exponential increase of wireless traffic are pushing the wireless community to investigate solutions to maximally exploit the available spectrum. Among the proposed solutions, the operation of Long Term Evolution (LTE) in the unlicensed spectrum (LTE-U) has attracted significant attention. Recently, the 3rd Generation Partnership Project announced specifications that allow LTE to transmit in the unlicensed spectrum using a Listen Before Talk (LBT) procedure, respecting this way the regulator requirements worldwide. However, the proposed standards may cause coexistence issues between LTE and legacy Wi-Fi networks. In this article, it is discussed that a fair coexistence mechanism is needed to guarantee equal channel access opportunities for the co-located networks in a technology-agnostic way, taking into account potential traffic requirements. In order to enable harmonious coexistence and fair spectrum sharing among LTE-U and Wi-Fi, an adaptive LTE-U LBT scheme is presented. This scheme uses a variable LTE transmission opportunity (TXOP) followed by a variable muting period. This way, co-located Wi-Fi networks can exploit the muting period to gain access to the wireless medium. The scheme is studied and evaluated in different compelling scenarios using a simulation platform. The results show that by configuring the LTE-U with the appropriate TXOP and muting period values, the proposed scheme can significantly improve the coexistence among LTE-U and Wi-Fi in a fair manner. Finally, a preliminary algorithm is proposed on how the optimal configuration parameters can be selected towards harmonious and fair coexistence

Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum

In the past few years, machine learning (ML) techniques have been extensively applied to provide efficient solutions to complex wireless network problems. As such, Convolutional Neural Network (CNN) and Q-learning based ML techniques are most popular to achieve harmonized coexistence of Wi-Fi with other co-located technologies such as LTE. In the existing coexistence schemes, a co-located technology selects its transmission time based on the level of Wi-Fi traffic generated in its collision domain which is determined by either sniffing the Wi-Fi packets or using a central coordinator that can communicate with the co-located networks to exchange their status and requirements through a collaboration protocol. However, such approaches for sensing traffic status increase cost, complexity, traffic overhead, and reaction time of the coexistence schemes. As a solution to this problem, this work applies a ML-based approach that is capable to determine the saturation status of a Wi-Fi network based on real-time and over-the-air collection of medium occupation statistics about the Wi-Fi frames without the need for decoding. In particular, inter-frame spacing statistics of Wi-Fi frames are used to develop a CNN model that can determine Wi-Fi network saturation. The results demonstrate that the proposed ML-based approach can accurately classify whether a Wi-Fi network is saturated or not

Enhancing the coexistence of LTE and Wi-Fi in unlicensed spectrum through convolutional neural networks

Over the last years, the ever-growing wireless traffic has pushed the mobile community to investigate solutions that can assist in more efficient management of the wireless spectrum. Towards this direction, the long-term evolution (LIE) operation in the unlicensed spectrum has been proposed. Targeting a global solution that respects the regional requirements, 3GPP announced the standard of LIE licensed assisted access (LAA). However, LIE LAA may result in unfair coexistence with Wi-Fi, especially when Wi-Fi does not use frame aggregation. Targeting a technique that enables fair channel access, the mLTE-U scheme has been proposed. According to mLTE-U, LTE uses a variable transmission opportunity, followed by a variable muting period that can be exploited by other networks to transmit. For the selection of the appropriate mLTE-U configuration, information about the dynamically changing wireless environment is required. To this end, this paper proposes a convolutional neural network (CNN) that is trained to perform identification of LIE and Wi-Fi transmissions. In addition, it can identify the hidden terminal effect caused by multiple LTE transmissions, multiple Wi-Fi transmissions, or concurrent LIE and Wi-Fi transmissions. The designed CNN has been trained and validated using commercial off-the-shelf LIE and Wi-Fi hardware equipment and for two wireless signal representations, namely, in-phase and quadrature samples and frequency domain representation through fast Fourier transform. The classification accuracy of the two resulting CNNs is tested for different signal to noise ratio values. The experimentation results show that the data representation affects the accuracy of CNN. The obtained information from CNN can be exploited by the mLTE-U scheme in order to provide fair coexistence between the two wireless technologies

Cooperation techniques between LTE in unlicensed spectrum and Wi-Fi towards fair spectral efficiency

On the road towards 5G, a proliferation of Heterogeneous Networks (HetNets) is expected. Sensor networks are of great importance in this new wireless era, as they allow interaction with the environment. Additionally, the establishment of the Internet of Things (IoT) has incredibly increased the number of interconnected devices and consequently the already massive wirelessly transmitted traffic. The exponential growth of wireless traffic is pushing the wireless community to investigate solutions that maximally exploit the available spectrum. Recently, 3rd Generation Partnership Project (3GPP) announced standards that permit the operation of Long Term Evolution (LTE) in the unlicensed spectrum in addition to the exclusive use of the licensed spectrum owned by a mobile operator. Alternatively, leading wireless technology developers examine standalone LTE operation in the unlicensed spectrum without any involvement of a mobile operator. In this article, we present a classification of different techniques that can be applied on co-located LTE and Wi-Fi networks. Up to today, Wi-Fi is the most widely-used wireless technology in the unlicensed spectrum. A review of the current state of the art further reveals the lack of cooperation schemes among co-located networks that can lead to more optimal usage of the available spectrum. This article fills this gap in the literature by conceptually describing different classes of cooperation between LTE and Wi-Fi. For each class, we provide a detailed presentation of possible cooperation techniques that can provide spectral efficiency in a fair manner

Adaptive CNN-based private LTE solution for fair coexistence with Wi-Fi in unlicensed spectrum

Recently, the expansion of wireless network deployments is resulting in increased scarcity of available licensed radio spectrum. As the domain of wireless communications is progressing rapidly, many industries are looking into wireless network solutions that can increase their productivity. Private LTE is a promising wireless network solution as it can be customised independently without the control of a mobile network operator while providing reliable and spectrum efficient services. For this reason, the deployment of Private LTE in the unlicensed spectrum and its coexistence with Wi-Fi is becoming a popular topic in research. In this paper, we propose a coexistence scheme for private LTE network in unlicensed spectrum that enables a fair spectrum sharing with co-located Wi-Fi networks. This is achieved by exploiting various LTE frame configurations consisting of different combinations of downlink, uplink, special subframe and muted subframes. The configuration of a single frame is decided based on a rule based algorithm that exploits Wi-Fi spectrum occupancy statistics that is obtained from a technology recognition system which is based on a Convolutional Neural Network. The performance of the proposed private LTE scheme and its coexistence with Wi-Fi is investigated for different traffic scenarios showcasing how the proposed scheme can lead to a harmless coexistence of LTE and Wi-Fi