633 research outputs found
On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time
Random Access Channel (RACH) has been identified as one of the major
bottlenecks for accommodating massive number of machine-to-machine (M2M) users
in LTE networks, especially for the case of burst arrival of connection
requests. As a consequence, the burst resolution problem has sparked a large
number of works in the area, analyzing and optimizing the average performance
of RACH. However, the understanding of what are the probabilistic performance
limits of RACH is still missing. To address this limitation, in the paper, we
investigate the reliability of RACH with access class barring (ACB). We model
RACH as a queuing system, and apply stochastic network calculus to derive
probabilistic performance bounds for burst resolution time, i.e., the worst
case time it takes to connect a burst of M2M devices to the base station. We
illustrate the accuracy of the proposed methodology and its potential
applications in performance assessment and system dimensioning.Comment: Presented at IEEE International Conference on Communications (ICC),
201
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
Frame Structure Design and Analysis for Millimeter Wave Cellular Systems
The millimeter-wave (mmWave) frequencies have attracted considerable
attention for fifth generation (5G) cellular communication as they offer orders
of magnitude greater bandwidth than current cellular systems. However, the
medium access control (MAC) layer may need to be significantly redesigned to
support the highly directional transmissions, ultra-low latencies and high peak
rates expected in mmWave communication. To address these challenges, we present
a novel mmWave MAC layer frame structure with a number of enhancements
including flexible, highly granular transmission times, dynamic control signal
locations, extended messaging and ability to efficiently multiplex directional
control signals. Analytic formulae are derived for the utilization and control
overhead as a function of control periodicity, number of users, traffic
statistics, signal-to-noise ratio and antenna gains. Importantly, the analysis
can incorporate various front-end MIMO capability assumptions -- a critical
feature of mmWave. Under realistic system and traffic assumptions, the analysis
reveals that the proposed flexible frame structure design offers significant
benefits over designs with fixed frame structures similar to current 4G
long-term evolution (LTE). It is also shown that fully digital beamforming
architectures offer significantly lower overhead compared to analog and hybrid
beamforming under equivalent power budgets.Comment: Submitted to IEEE Transactions for Wireless Communication
LTE-LAA ์ฑ๋ฅ ํฅ์์ ์ํ MAC ๊ณ์ธต ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2019. 2. ์ต์ฑํ.3GPP long term evolution (LTE) operation in unlicensed spectrum is emerging as a promising technology in achieving higher data rate with LTE since ultra-wide unlicensed spectrum, e.g., about 500 MHz at 5โ6 GHz range, is available in most countries. Recently, 3GPP has finalized standardization of licensed-assisted access (LAA) for LTE operation in 5 GHz unlicensed spectrum, which has been a playground only for Wi-Fi.
In this dissertation, we propose the following three strategies to enhance the performance of LAA: (1) Receiver-aware COT adaptation, (2) Collision-aware link adaptation, and (3) Power and energy detection threshold adaptation.
First, LAA has a fixed maximum channel occupancy time (MCOT), which is the maximum continuous transmission time after channel sensing, while Wi-Fi may transmit for much shorter time duration. As a result, when Wi-Fi coexists with LAA, Wi-Fi airtime and throughput can be much less than those achieved when Wi-Fi coexists with another Wi-Fi. To guarantee fair airtime and improve throughput of Wi-Fi, we propose a receiver-aware channel occupancy time (COT) adaptation ( RACOTA ) algorithm, which observes Wi-Fi aggregate MAC protocol data unit (A-MPDU) frames and matches LAAs COT to the duration of A-MPDU frames when any Wi-Fi receiver has more data to receive. Moreover, RACOTA detects saturation of Wi-Fi traffic and adjusts COT only if Wi-Fi traffic is saturated. We prototype saturation detection algorithm of RACOTA with commercial off-the-shelf Wi-Fi device and show that RACOTA detects saturation of Wi-Fi networks accurately. Through ns-3 simulations, we demonstrate that RACOTA provides airtime fairness between LAA and Wi-Fi while achieves up to 334% Wi-Fi throughput gain.
Second, the link adaptation scheme of the conventional LTE, adaptive modulation and coding (AMC), cannot operate well in the unlicensed band due to intermittent collisions. Intermittent collisions make LAA eNB lower modulation and coding scheme (MCS) for the subsequent transmission and such unnecessarily lowered
MCS significantly degrades spectral efficiency. To address this problem, we propose a collision-aware link adaptation algorithm ( COALA ). COALA exploits k-means unsupervised clustering algorithm to discriminate channel quality indicator (CQI) reports which are measured with collision interference and selects the most suitable MCS for the next transmission. By prototype-based experiments, we demonstrate that COALA detects collisions accurately, and by conducting ns-3 simulations in various scenarios, we also show that COALA achieves up to 74.9% higher user perceived throughput than AMC.
Finally, we propose PETAL to mitigate the negative impact of spatial reuse (SR) operation. We first design the baseline algorithm, which operates SR aggressively, and show that the baseline algorithm degrades the throughput performance severely when the UE is close to an interferer. Our proposed algorithm PETAL estimates and compares the spectral efficiency for the SR operation and non-SR operation. Then, PETAL operates SR only if the spectral efficiency of SR operation is expected to be higher than the case of non-SR operation. Our simulation verifies the performance of PETAL in various scenarios. When two pair of an eNB and a UE coexists, PETAL improves the throughput by up to 329% over the baseline algorithm.
In summary, we identify interesting problems that appeared with LAA and shows the impact of the problems through the extensive simulations and propose compelling algorithms to solve the problems. The airtime fairness between Wi-Fi and LAA is improved with COT adaptation. Furthermore, link adaptation accuracy and SR operation are improved by exploiting CQI reports history. The performance of the proposed schemes is verified by system level simulation.๋น๋ฉดํ ๋์ญ์์์ LTE ๋์์ ๋ ๋์ ๋ฐ์ดํฐ ์ ์ก๋ฅ ์ ๋ฌ์ฑํ๋ ์ ๋งํ ๊ธฐ์ ๋ก ๋ถ๊ฐ๋๊ณ ์๋ค. ์ต๊ทผ 3GPP๋ ๊ธฐ์กด Wi-Fi ๊ธฐ์ ์ด ์ฌ์ฉํ๋ 5 GHz ๋น๋ฉดํ ๋์ญ์์ LTE๋ฅผ ์ฌ์ฉํ๋ licensed-assisted access (LAA) ๊ธฐ์ ์ ํ์คํ๋ฅผ ์๋ฃํ์๋ค. ๋ณธ ๋
ผ๋ฌธ์์ ์ฐ๋ฆฌ๋ LAA์ ์ฑ๋ฅ์ ํฅ์์ํค๊ธฐ ์ํด ๋ค์๊ณผ ๊ฐ์ ์ธ ๊ฐ์ง ์ ๋ต์ ์ ์ํ๋ค: (1) ์์ ๊ธฐ ์ธ์ ์ฑ๋ ์ ์ ์๊ฐ ์ ์, (2) ์ถฉ๋ ์ธ์ ๋งํฌ ์ ์, (3) ์ ๋ ฅ ๋ฐ ์๋์ง ๊ฒ์ถ ์ญ์น ์ ์.
์ฒซ์งธ, LAA๋ ๊ณ ์ ๋ ์ต๋ ์ฑ๋ ์ ์ ์๊ฐ์ ๊ฐ์ง๊ณ ์๊ณ ๊ทธ ์๊ฐ ๋งํผ ์ฐ์์ ์ผ๋ก ์ ์กํ ์ ์๋ ๋ฐ๋ฉด, Wi-Fi๋ ๋น๊ต์ ์งง์ ์๊ฐ ๋์๋ง ์ฐ์์ ์ผ๋ก ์ ์กํ ์ ์๋ค. ๊ทธ ๊ฒฐ๊ณผ Wi-Fi๊ฐ LAA์ ๊ณต์กดํ ๋ Wi-Fi์ airtime๊ณผ ์์จ ์ฑ๋ฅ์ Wi-Fi๊ฐ ๋ ๋ค๋ฅธ Wi-Fi์ ๊ณต์กดํ ๋์ ๋นํ์ฌ ์ ํ๋๊ฒ๋๋ค. ๋ฐ๋ผ์ ์ฐ๋ฆฌ๋ Wi-Fi์ airtime๊ณผ ์์จ ์ฑ๋ฅ์ ํฅ์์ํค๊ธฐ ์ํ์ฌ Wi-Fi์ A-MPDU ํ๋ ์ ์ ์ก ์๊ฐ์ ๋ง์ถ์ด LAA์ ์ฑ๋ ์ ์ ์๊ฐ์ ์กฐ์ ํ๋ ์์ ๊ธฐ ์ธ์ ์ฑ๋ ์ ์ ์๊ฐ ์ ์ ๊ธฐ๋ฒ์ธ RACOTA๋ฅผ ์ ์ํ๋ค. RACOTA ๋ ํฌํ ๊ฐ์ง ๊ฒฐ๊ณผ Wi-Fi ์์ ๊ธฐ๊ฐ ๋ ๋ฐ์ ๋ฐ์ดํฐ๊ฐ ์๋ค๊ณ ํ๋จ๋ ๋์๋ง ์ฑ๋ ์ ์ ์๊ฐ์ ์กฐ์ ํ๋ค. ์ฐ๋ฆฌ๋ RACOTA ์ ํฌํ ๊ฐ์ง ์๊ณ ๋ฆฌ์ฆ์ ์์ฉ Wi-Fi ์ฅ๋น์ ๊ตฌํํ์ฌ ์ด๋ฅผ ๋ฐํ์ผ๋ก ์ค์ธก์ ํตํด RACOTA ๊ฐ ๊ณต์กดํ๋ Wi-Fi์ ํฌํ ์ฌ๋ถ๋ฅผ ์ ํํ๊ฒ ๊ฐ์งํด๋์ ๋ณด์ธ๋ค. ๋ํ ์ฐ๋ฆฌ๋ ns-3 ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ RACOTA ๋ฅผ ์ฌ์ฉํ๋ LAA๊ฐ ๊ณต์กดํ๋ Wi-Fi์๊ฒ ๊ณต์ ํ airtime์ ์ ๊ณตํ๊ณ ๊ธฐ์กด LAA์ ๊ณต์กดํ๋ Wi-Fi ๋๋น ์ต๋ 334%์ Wi-Fi ์์จ ์ฑ๋ฅ ํฅ์์ ๊ฐ์ ธ์ด์ ๋ณด์ธ๋ค.
๋์งธ, ๊ฐํ์ ์ธ ์ถฉ๋์ด ๋ฐ์ํ ์ ์๋ ๋น๋ฉดํ ๋์ญ์์๋ ๊ธฐ์กด LTE์ ๋งํฌ ์ ์ ๊ธฐ๋ฒ์ธ adaptive modulation and coding (AMC)์ด ์ ๋์ํ์ง ์์ ์ ์๋ค. ๊ฐํ์ ์ธ ์ถฉ๋์ LAA ๊ธฐ์ง๊ตญ์ผ๋ก ํ์ฌ๊ธ modulation and coding scheme (MCS)์ ๋ฎ์ถ์ด์ ๋ค์ ์ ์ก์ ํ๋๋ก ํ๋๋ฐ ๋ค์ ์ ์ก ์์ ์ถฉ๋์ด ๋ฐ์ํ์ง ์๋๋ค๋ฉด ๋ถํ์ํ๊ฒ ๋ฎ์ถ MCS๋ก ์ธํด ์ฃผํ์ ํจ์จ์ด ํฌ๊ฒ ์ ํ๋๋ค. ์ด๋ฌํ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ์ํด ์ฐ๋ฆฌ๋ ์ถฉ๋ ์ธ์ ๋งํฌ ์ ์ ๊ธฐ๋ฒ์ธ COALA ๋ฅผ ์ ์ํ๋ค. COALA ๋ k-means ๋ฌด๊ฐ๋
ํด๋ฌ์คํฐ๋ง ์๊ณ ๋ฆฌ์ฆ์ ์ฌ์ฉํ์ฌ channel quality indicator (CQI) ๋ฆฌํฌํธ ์ค ์ถฉ๋ ๊ฐ์ญ์ ์ํฅ์ ๋ฐ์ CQI ๋ฆฌํฌํธ๋ค์ ๊ตฌ๋ณํด๋ด๊ณ ์ด๋ฅผ ํตํด ๋ค์ ์ ์ก์ ์ํ ์ต์ ์ MCS๋ฅผ ์ ํํ๋ค. ์ฐ๋ฆฌ๋ ์ค์ธก์ ํตํ์ฌ COALA ๊ฐ ์ ํํ๊ฒ ์ถฉ๋์ ๊ฐ์งํด๋์ ๋ณด์ธ๋ค. ๋ํ ์ฐ๋ฆฌ๋ ๋ค์ํ ํ๊ฒฝ์์์ ns-3 ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ COALA ๊ฐ AMC ๋๋น ์ต๋ 74.9%์ ์ฌ์ฉ์ ์ธ์ ์์จ ์ฑ๋ฅ ํฅ์์ ๊ฐ์ ธ์ด์ ๋ณด์ธ๋ค.
์
์งธ, ์ฐ๋ฆฌ๋ ๊ณต๊ฐ ์ฌ์ฌ์ฉ ๋์์ ๋ถ์์ฉ์ ์ต์ํํ๊ธฐ ์ํ์ฌ ์์ ๋จ๋ง์ ๊ณ ๋ คํ์ฌ ์ ์ก ํ์ ๋ฐ ์๋์ง ๊ฒ์ถ ์ญ์น๋ฅผ ์ ์์ ์ผ๋ก ์กฐ์ ํ๋ PETAL ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ฐ๋ฆฌ๋ ๋จผ์ ์์ ๋จ๋ง์ ๊ณ ๋ คํ์ง ์๊ณ ๊ณต๊ฒฉ์ ์ผ๋ก ๊ณต๊ฐ ์ฌ์ฌ์ฉ ๋์์ ํ๋ baseline ์๊ณ ๋ฆฌ์ฆ์ ์ค๊ณํ๊ณ ๋ค์ํ ํ๊ฒฝ์์์ ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ ์์ ๋จ๋ง์ด ๊ฐ์ญ์์ ๊ฐ๊น์ด ๊ฒฝ์ฐ baseline ์๊ณ ๋ฆฌ์ฆ์ ์ฑ๋ฅ์ด ์ฌ๊ฐํ๊ฒ ์ดํ๋จ์ ๋ณด์ธ๋ค. ์ ์ํ๋ ์๊ณ ๋ฆฌ์ฆ์ธ PETAL ์ ์์ ๋จ๋ง๋ก๋ถํฐ ๋ฐ์ CQI ๋ฆฌํฌํธ ์ ๋ณด์ ์ฑ๋ ์ ์ ์์ ๊น์ง์ ํ๊ท ๋๊ธฐ ์๊ฐ์ ์ด์ฉํ์ฌ ๊ณต๊ฐ ์ฌ์ฌ์ฉ ๋์์ ํ ๋ ์์๋๋ ์ฃผํ์ ํจ์จ๊ณผ ๊ณต๊ฐ ์ฌ์ฌ์ฉ ๋์์ ํ์ง ์์ ๋ ์์๋๋ ์ฃผํ์ ํจ์จ์ ๋น๊ตํ์ฌ ๊ณต๊ฐ ์ฌ์ฌ์ฉ ๋์์ ํ ๋ ์์๋๋ ์ฃผํ์ ํจ์จ์ด ๋ ํด ๋์๋ง ๊ณต๊ฐ ์ฌ์ฌ์ฉ ๋์์ ํ๋ค. ์ฐ๋ฆฌ๋ ๋ค์ํ ํ๊ฒฝ์์์ ns-3 ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ PETAL ์ด baseline ์๊ณ ๋ฆฌ์ฆ ๋๋น ์ต๋ 329%์ ์์จ ์ฑ๋ฅ ํฅ์์ ๊ฐ์ ธ์ด์ ๋ณด์ธ๋ค.
์์ฝํ์๋ฉด, ์ฐ๋ฆฌ๋ LAA์ ๋ฑ์ฅ๊ณผ ํจ๊ป ์๋กญ๊ฒ ๋๋๋๋ ํฅ๋ฏธ๋ก์ด ๋ฌธ์ ๋ค์ ํ์ธํ๊ณ ๋ฌธ์ ๋ค์ ์ฌ๊ฐ์ฑ์ ๋ค์ํ ํ๊ฒฝ์์์ ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ ์ดํด๋ณด๊ณ ์ด ๋ฌํ ๋ฌธ์ ๋ค์ ํด๊ฒฐํ ์ ์๋ ์๊ณ ๋ฆฌ์ฆ๋ค์ ์ ์ํ๋ค. Wi-Fi์ LAA ์ฌ์ด์ airtime ๊ณต์ ์ฑ์ LAA์ ์ฐ์ ์ ์ก ์๊ฐ์ ์ ์์ ์ผ๋ก ์กฐ์ ํ์ฌ ๊ฐ์ ํ ์ ์๋ค. ๋ํ ๋งํฌ ์ ์ ์ ํ๋์ ๊ณต๊ฐ ์ฌ์ฌ์ฉ ๋์์ ํจ์จ์ฑ์ CQI ๋ฆฌํฌํธ๋ค์ ๋ถํฌ๋ฅผ ์ด์ฉํ์ฌ ๊ฐ์ ํ ์ ์๋ค. ์ ์ํ๋ ์๊ณ ๋ฆฌ์ฆ๋ค์ ์ฑ๋ฅ์ ์์คํ
๋ ๋ฒจ ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ ๊ฒ์ฆ๋์๋ค.1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Overview of Existing Approaches . . . . . . . . . . . . . . . . . . . 2
1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3.1 RACOTA: Receiver-Aware Channel Occupancy Time Adaptation for LTE-LAA . . . . . . . 2
1.3.2 COALA: Collision-Aware Link Adaptation for LTE-LAA . . 3
1.3.3 PETAL: Power and Energy Detection Threshold Adaptation for LAA . . . . . . . . . . . . . . 4
1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . 5
2 RACOTA:Receiver-AwareChannelOccupancyTimeAdaptationforLTE- LAA 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 MAC Mechanisms of Wi-Fi and LAA . . . . . . . . . . . . . . . . . 10
2.3.1 Wi-Fi MAC Operation . . . . . . . . . . . . . . . . . . . . . 10
2.3.2 LAA Listen-Before-Talk (LBT) Mechanism . . . . . . . . . . 11
2.3.3 Wide Bandwidth Operation . . . . . . . . . . . . . . . . . . 13
2.4 Coexistence performance of LAA and Wi-Fi . . . . . . . . . . . . . . 14
2.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4.2 Unfairness between LAA and Wi-Fi . . . . . . . . . . . . . . 15
2.5 Receiver-Aware COT Adaptation Algorithm . . . . . . . . . . . . . . 17
2.5.1 Saturation Detection (SD) . . . . . . . . . . . . . . . . . . . 20
2.5.2 Receiver-Aware COT Decision . . . . . . . . . . . . . . . . . 22
2.6 Performance Evaluation of SD Algorithm . . . . . . . . . . . . . . . 22
2.6.1 Measurement Setup . . . . . . . . . . . . . . . . . . . . . . . 22
2.6.2 PPDUMaxTime Detection . . . . . . . . . . . . . . . . . . . 24
2.6.3 Saturation Detection Performance . . . . . . . . . . . . . . . 26
2.7 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.7.1 Saturated Traffic Scenario . . . . . . . . . . . . . . . . . . . 28
2.7.2 Unsaturated Traffic Scenario . . . . . . . . . . . . . . . . . . 30
2.7.3 Bursty Traffic Scenario . . . . . . . . . . . . . . . . . . . . . 30
2.7.4 Heterogeneous Wi-Fi Traffic Generation Scenario . . . . . . 31
2.7.5 Multiple Node Scenario . . . . . . . . . . . . . . . . . . . . 34
2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3 COALA: Collision-Aware Link Adaptation for LTE-LAA 36
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Backgound and Related Work . . . . . . . . . . . . . . . . . . . . . 38
3.2.1 LAA and LBT . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2.2 AMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.3 Inter-Cell Interference Cancellation . . . . . . . . . . . . . . 39
3.2.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 Impact of Collision to Link Adaptation . . . . . . . . . . . . . . . . . 41
3.4 COALA: Collision-aware Link Adaptation . . . . . . . . . . . . . . . 47
3.4.1 CQI Clustering Algorithm . . . . . . . . . . . . . . . . . . . 48
3.4.2 Collision Detection and Collision Probability Estimation . . . 48
3.4.3 Suitable MCS Selection . . . . . . . . . . . . . . . . . . . . 49
3.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.5.1 Prototype-based Feasibility Study . . . . . . . . . . . . . . . 51
3.5.2 Contention Collision with LAA eNBs . . . . . . . . . . . . . 53
3.5.3 Hidden Collision . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5.4 Bursty Hidden Collision . . . . . . . . . . . . . . . . . . . . 58
3.5.5 Contention Collision with Wi-Fi Transmitters . . . . . . . . . 58
3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4 PETAL: Power and Energy Detection Threshold Adaptation for LAA 62
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2 Backgound and Related Work . . . . . . . . . . . . . . . . . . . . . 64
4.2.1 Energy Detection Threshold . . . . . . . . . . . . . . . . . . 64
4.2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Baseline Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.3.1 Design of the Baseline Algorithm . . . . . . . . . . . . . . . 65
4.3.2 Performance of the Baseline Algorithm . . . . . . . . . . . . 66
4.4 PETAL: Power and Energy Detection Threshold Adaptation . . . . . 68
4.4.1 CQI Management . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4.2 Success Probability and Airtime Ratio Estimation . . . . . . . 69
4.4.3 CQI Clustering Algorithm . . . . . . . . . . . . . . . . . . . 71
4.4.4 SR Decision . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.5.1 Two Cell Scenario . . . . . . . . . . . . . . . . . . . . . . . 73
4.5.2 Coexistence with Standard LAA . . . . . . . . . . . . . . . . 75
4.5.3 Four Cell Scenario . . . . . . . . . . . . . . . . . . . . . . . 76
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5 Concluding Remarks 79
5.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 79
Abstract (In Korean) 88
๊ฐ์ฌ์ ๊ธ 92Docto
A Tractable Model of the LTE Access Reservation Procedure for Machine-Type Communications
A canonical scenario in Machine-Type Communications (MTC) is the one
featuring a large number of devices, each of them with sporadic traffic. Hence,
the number of served devices in a single LTE cell is not determined by the
available aggregate rate, but rather by the limitations of the LTE access
reservation protocol. Specifically, the limited number of contention preambles
and the limited amount of uplink grants per random access response are crucial
to consider when dimensioning LTE networks for MTC. We propose a low-complexity
model of LTE's access reservation protocol that encompasses these two
limitations and allows us to evaluate the outage probability at click-speed.
The model is based chiefly on closed-form expressions, except for the part with
the feedback impact of retransmissions, which is determined by solving a fixed
point equation. Our model overcomes the incompleteness of the existing models
that are focusing solely on the preamble collisions. A comparison with the
simulated LTE access reservation procedure that follows the 3GPP
specifications, confirms that our model provides an accurate estimation of the
system outage event and the number of supported MTC devices.Comment: Submitted, Revised, to be presented in IEEE Globecom 2015; v3: fixed
error in eq. (4
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