Delivering Fairness and QoS Guarantees for LTE/Wi-Fi Coexistence Under LAA Operation

Maule, Massimiliano; Moltchanov, Dmitri; Kustarev, Pavel; Komarov, Mikhail; Andreev, Sergey; Koucheryavy, Yevgeni

doi:10.1109/access.2018.2793941

Cited by 38 publications

(20 citation statements)

References 25 publications

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“…In addition to QoS, fairness is another metric exploited by classical optimization techniques. The first algorithm proposed in [23], based on connection admission control (CAC), is responsible for fairness in the sharing of resources. The second algorithm uses a duty cycle approach, similar to the one proposed for LTE-U, but applied to LTE-LAA to improve delay related metrics.…”

Section: Related Workmentioning

confidence: 99%

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Neto

Ana

et al. 2020

Sensors

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Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.

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Section: Related Workmentioning

confidence: 99%

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Neto

Ana

et al. 2020

Sensors

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“…In spite of significant efforts devoted to development of 3GPP-compliant LAA system the performance assessment framework has been proposed in [20] only. The authors have proposed schedule-based design of the LAA MAC layer with CAC mechanism and dynamic duty cycle adaptation to provide both throughput and delay requirements to LAA sessions.…”

Section: A Related Workmentioning

confidence: 99%

“…In this paper, we consider duty-cycle based LAA architecture initially proposed in [19] and [20]. The proposed channel access procedure is illustrated in Fig.…”

Section: B the Schedule-based Laa Architecturementioning

confidence: 99%

Performance Assessment of QoS-Aware LTE Sessions Offloading Onto LAA/WiFi Systems

et al. 2019

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The publication has been prepared with the support of the ''RUDN University Program 5-100.'' ABSTRACT Fifth-generation (5G) cellular systems are expected to orchestrate a variety of air interfaces. In this heterogeneous setting 4G, LTE systems will remain of special importance providing service over a relatively large area not covered with the 3GPP new radio technology. However, the scarcity of radio resources below 6 GHz as well as constantly increasing user traffic demands call for efficient offloading strategy from the LTE network. In this paper, we consider and compare two LTE session offloading strategies, including LAA and WiFi offloading. Although by design, the LAA system supports qualityof-service (QoS) guarantees in terms of minimum throughput, the assured performance might still be violated by resource reallocations when satisfying the compulsory fairness criteria. To assess and compare the performance of these schemes, we develop an analytical framework that takes into account specifics of duty-cycle schedule-based Licensed Assisted LTE Access (LAA) system with connection admission control (CAC), resource reallocation mechanism, and realistic elastic traffic nature. As a special case of the developed framework, we also address the offloading of QoS-aware LTE sessions into conventional WiFi. Our numerical results indicate that WiFi offloading is characterized by a significantly greater number of offloaded sessions. However, this advantage comes at the expense of severe performance degradation in terms of minimum throughput guarantees. Implementing CAC procedures, LAA efficiently addresses this problem. We conclude that WiFi offloading of QoS-aware LTE sessions can be used in light traffic conditions only. On the other hand, offloading to the schedule-based LAA system allows preserving QoS over a wide range of arrival traffic statistics.

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“…In [16] conventional LTE frame structure consist minimum of 10 subframes, each subframe has 1ms length and maximum of 40 subframes. By increasing the number of subframes that leads to the decrement of subframe length, which has less than 1ms duration.…”

Section: Lte Frame Structurementioning

confidence: 99%

Optimum Frequency Allocation Mechanism of TV White Spaces by using Interval Graph

2019

IJEAT

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The unused frequencies of terrestrial TV are being explored in the recent years, to satisfy the bandwidth demands of ever-increasing wireless communication systems. Allocating these unused TV frequencies is often a challenging task. Allocations that fulfil the user requirements at various instants, while maximizing the utilization of available TV frequencies is the one that is desired. In this paper, interval-graph method is implemented to identify the optimum number of channels needed for the given demand of bandwidths. Simulations are carried out by using GLPK 4.65 solver. Results show the appropriate number of channels required or conversely the reduction of data rates to individual users based on the available bandwidths.

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Delivering Fairness and QoS Guarantees for LTE/Wi-Fi Coexistence Under LAA Operation

Cited by 38 publications

References 25 publications

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Performance Assessment of QoS-Aware LTE Sessions Offloading Onto LAA/WiFi Systems

Optimum Frequency Allocation Mechanism of TV White Spaces by using Interval Graph

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