469 research outputs found

    Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE

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    In cellular massive Machine-Type Communications (MTC), a device can transmit directly to the base station (BS) or through an aggregator (intermediate node). While direct device-BS communication has recently been in the focus of 5G/3GPP research and standardization efforts, the use of aggregators remains a less explored topic. In this paper we analyze the deployment scenarios in which aggregators can perform cellular access on behalf of multiple MTC devices. We study the effect of packet bundling at the aggregator, which alleviates overhead and resource waste when sending small packets. The aggregators give rise to a tradeoff between access congestion and resource starvation and we show that packet bundling can minimize resource starvation, especially for smaller numbers of aggregators. Under the limitations of the considered model, we investigate the optimal settings of the network parameters, in terms of number of aggregators and packet-bundle size. Our results show that, in general, data aggregation can benefit the uplink massive MTC in LTE, by reducing the signalling overhead.Comment: to appear in IEEE Networ

    Dynamic RACH Partition for Massive Access of Differentiated M2M Services

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    In machine-to-machine (M2M) networks, a key challenge is to overcome the overload problem caused by random access requests from massive machine-type communication (MTC) devices. When differentiated services coexist, such as delay-sensitive and delay-tolerant services, the problem becomes more complicated and challenging. This is because delay-sensitive services often use more aggressive policies, and thus, delay-tolerant services get much fewer chances to access the network. To conquer the problem, we propose an efficient mechanism for massive access control over differentiated M2M services, including delay-sensitive and delay-tolerant services. Specifically, based on the traffic loads of the two types of services, the proposed scheme dynamically partitions and allocates the random access channel (RACH) resource to each type of services. The RACH partition strategy is thoroughly optimized to increase the access performances of M2M networks. Analyses and simulation demonstrate the effectiveness of our design. The proposed scheme can outperform the baseline access class barring (ACB) scheme, which ignores service types in access control, in terms of access success probability and the average access delay

    ๋น„๋ฉดํ—ˆ๋Œ€์—ญ ์…€๋ฃฐ๋ผ ํ†ต์‹ ์˜ ์„ฑ๋Šฅ ๋ถ„์„ ๋ฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ธฐ๋ฒ• ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021. 2. ๋ฐ•์„ธ์›….3GPP๋Š” LAA (licensed-assisted access)๋ผ๊ณ ํ•˜๋Š” 5GHz ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ LTE๋ฅผ ๊ฐœ๋ฐœํ–ˆ์Šต๋‹ˆ๋‹ค. LAA๋Š” ์ถฉ๋Œ ๋ฐฉ์ง€ ๊ธฐ๋Šฅ์„ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•ด Wi-Fi์˜ CSMA / CA (Carrier Sense Multiple Access with Collision avoidance)์™€ ์œ ์‚ฌํ•œ LBT (Listen Before Talk) ์ž‘์—…์„ ์ฑ„ํƒํ•˜์—ฌ ๊ฐ LAA ๋‹ค์šด ๋งํฌ ๋ฒ„์ŠคํŠธ์˜ ํ”„๋ ˆ์ž„ ๊ตฌ์กฐ ์˜ค๋ฒ„ ํ—ค๋“œ๋Š” ๊ฐ๊ฐ์˜ ์ข…๋ฃŒ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง‘๋‹ˆ๋‹ค. ์ด์ „ LBT ์ž‘์—…. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ ์…€๋ฃฐ๋Ÿฌ ํ†ต์‹ ์„ ๋ถ„์„ํ•˜๊ธฐ์œ„ํ•œ ์ˆ˜์น˜ ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ ์…€๋ฃฐ๋Ÿฌ ํ†ต์‹ ์˜ ๋‹ค์Œ ๋‘ ๊ฐ€์ง€ ํ–ฅ์ƒ๋œ ๊ธฐ๋Šฅ์„ ๊ณ ๋ คํ•ฉ๋‹ˆ๋‹ค. ๋Œ€์—ญ ๋…๋ฆฝํ˜• ์…€๋ฃฐ๋Ÿฌ ํ†ต์‹ . ๊ธฐ์กด WiFi ๋ถ„์„ ๋ชจ๋ธ๋กœ๋Š” LAA์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ์ ์„ ๊ฐ์•ˆํ•˜์—ฌ ๋ณธ ์„œ์‹ ์—์„œ๋Š” ์—ฌ๋Ÿฌ ๊ฒฝํ•ฉ ์ง„ํ™” ๋œ NodeB๋กœ ๊ตฌ์„ฑ๋œ LAA ๋„คํŠธ์›Œํฌ์˜ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•˜๊ธฐ์œ„ํ•œ ์ƒˆ๋กœ์šด Markov ์ฒด์ธ ๊ธฐ๋ฐ˜ ๋ถ„์„ ๋ชจ๋ธ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. LAA ํ”„๋ ˆ์ž„ ๊ตฌ์กฐ ์˜ค๋ฒ„ ํ—ค๋“œ์˜ ๋ณ€ํ˜•. LTE-LAA๋Š” LTE์—์„œ ์ƒ์† ๋œ ์†๋„ ์ ์‘ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์œ„ํ•ด ์ ์‘ ๋ณ€์กฐ ๋ฐ ์ฝ”๋”ฉ (AMC) ์„ ์ฑ„ํƒํ•ฉ๋‹ˆ๋‹ค. AMC๋Š” ์ง„ํ™” ๋œ nodeB (eNB)๊ฐ€ ํ˜„์žฌ ์ „์†ก์˜ ์ฑ„๋„ ํ’ˆ์งˆ ํ‘œ์‹œ๊ธฐ ํ”ผ๋“œ ๋ฐฑ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋‹ค์Œ ์ „์†ก์„์œ„ํ•œ ๋ณ€์กฐ ๋ฐ ์ฝ”๋”ฉ ๋ฐฉ์‹ (MCS)์„ ์„ ํƒํ•˜๋„๋ก ๋•์Šต๋‹ˆ๋‹ค. ๋ผ์ด์„ ์Šค ๋Œ€์—ญ์—์„œ ๋™์ž‘ํ•˜๋Š” ๊ธฐ์กด LTE์˜ ๊ฒฝ์šฐ ๋…ธ๋“œ ๊ฒฝํ•ฉ ๋ฌธ์ œ๊ฐ€ ์—†์œผ๋ฉฐ AMC ์„ฑ๋Šฅ ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ž˜ ์ง„ํ–‰๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ์—์„œ ๋™์ž‘ํ•˜๋Š” LTE-LAA ์˜ ๊ฒฝ์šฐ ์ถฉ๋Œ ๋ฌธ์ œ๋กœ ์ธํ•ด AMC ์„ฑ๋Šฅ์ด ์ œ๋Œ€๋กœ ์ฒ˜๋ฆฌ๋˜์ง€ ์•Š์•˜์Šต๋‹ˆ๋‹ค. ์ด ํŽธ์ง€์—์„œ๋Š” AMC ์šด์˜์„ ๊ณ ๋ คํ•œ ํ˜„์‹ค์ ์ธ ์ฑ„๋„ ๋ชจ๋ธ์—์„œ LTELAA ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•˜๊ธฐ์œ„ํ•œ ์ƒˆ๋กœ ์šด Markov ์ฒด์ธ ๊ธฐ๋ฐ˜ ๋ถ„์„ ๋ชจ๋ธ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๋ฌด์„  ๋„คํŠธ์›Œํฌ ๋ถ„์„์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” Rayleigh ํŽ˜์ด๋”ฉ ์ฑ„๋„ ๋ชจ๋ธ์„ ์ฑ„ํƒํ•˜๊ณ  ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ns-3 ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ์—์„œ ์–ป์€ ๊ฒฐ๊ณผ ์™€ ๋น„๊ตํ•ฉ๋‹ˆ๋‹ค. ๋น„๊ต ๊ฒฐ๊ณผ๋Š” ํ‰๊ท  ์ •ํ™•๋„๊ฐ€ 99.5%๋กœ ๋ถ„์„ ๋ชจ๋ธ์˜ ์ •ํ™•๋„๋ฅผ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ๋†’์€ ๋ฐ์ดํ„ฐ ์†๋„์— ๋Œ€ํ•œ ์š”๊ตฌ ์‚ฌํ•ญ์œผ๋กœ ์ธํ•ด 3GPP๋Š” LTE-LAA๋ฅผ์œ„ํ•œ ๋‹ค์ค‘ ๋ฐ˜์†กํŒŒ ์šด์˜์„ ์ œ๊ณตํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋‹ค์ค‘ ๋ฐ˜์†กํŒŒ ๋™์ž‘์€ OOBE์— ์ทจ์•ฝํ•˜๊ณ  ์ œํ•œ๋œ ์ „์†ก ์ „๋ ฅ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋น„ํšจ์œจ์  ์ธ ์ฑ„๋„ ์‚ฌ์šฉ์„ ์ดˆ๋ž˜ํ•ฉ๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ฑ„๋„ ํšจ์œจ์„ ๋†’์ด๊ธฐ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋‹ค์ค‘ ๋ฐ˜์†กํŒŒ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ์ œ์•ˆํ•œ ๋ฐฉ์‹์€ ์ „์†ก ๋ฒ„์ŠคํŠธ๋ฅผ ์—ฌ๋Ÿฌ ๊ฐœ๋กœ ๋ถ„ํ• ํ•˜๊ณ  ์ „์†ก ์ „๋ ฅ ์ œํ•œ์„ ์ถฉ์กฑํ•˜๋ฉด์„œ ์งง์€ ์„œ๋ธŒ ํ”„๋ ˆ์ž„ ์ „์†ก ์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ ์ฑ„๋„ ์ƒํƒœ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ํŒ๋‹จํ•˜์—ฌ OOBE ๋ฌธ์ œ๋ฅผ ๊ทน๋ณต ํ•  ์ˆ˜์žˆ๋Š” ์—๋„ˆ์ง€ ๊ฐ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์†Œํ”„ํŠธ์›จ์–ด ์ •์˜ ๋ผ๋””์˜ค๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ํ”„๋กœํ†  ํƒ€์ž…์€ 99% ์ด์ƒ์˜ ์ •ํ™•๋„๋กœ ์ฑ„๋„ ์ƒํƒœ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ์—๋„ˆ์ง€ ๊ฐ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์‹คํ–‰ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ns-3 ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆ ๋œ ๋‹ค์ค‘ ๋ฐ˜์†กํŒŒ ์•ก์„ธ์Šค ๋ฐฉ์‹์ด ๊ธฐ์กด LBT ์œ ํ˜• A ๋ฐ ์œ ํ˜• B์— ๋น„ํ•ด ์‚ฌ์šฉ์ž์ธ์ง€ ์ฒ˜๋ฆฌ๋Ÿ‰์—์„œ ๊ฐ๊ฐ ์ตœ๋Œ€ 59% ๋ฐ 21.5%์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ๋‹ฌ์„ฑ ํ•จ์„ ํ™•์ธํ–ˆ์Šต๋‹ˆ๋‹ค. ๋ ˆ๊ฑฐ์‹œ LAA์—๋Š” ๋ฐฐํฌ ๋ฌธ์ œ๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— 3GPP์™€ MulteFire ์–ผ๋ผ์ด์–ธ์Šค๋Š” ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ ๋…๋ฆฝํ˜• ์…€๋ฃฐ๋Ÿฌ ํ†ต์‹  ์‹œ์Šค ํ…œ์„ ์ œ์•ˆํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ข…๋ž˜์˜ ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ ๋…๋ฆฝํ˜• ์…€๋ฃฐ๋Ÿฌ ํ†ต์‹  ์‹œ์Šคํ…œ์€ ์ƒํ–ฅ ๋งํฌ ์ œ์–ด ๋ฉ”์‹œ์ง€์˜ ์ „์†ก ํ™•๋ฅ ์ด ๋‚ฎ๋‹ค. ์ด ๋…ผ๋ฌธ์€ Wi-Fi ๋ธ”๋ก ACK ํ”„๋ ˆ์ž„์— ์—… ๋งํฌ ์ œ์–ด ๋ฉ”์‹œ์ง€๋ฅผ ๋„ฃ๋Š” W ARQ : Wi-Fi ์ง€์› HARQ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ W-ARQ์˜ ์ฒ˜ ๋ฆฌ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋ณ‘๋ ฌ HARQ ๋ฐ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๋œ Minstrel์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ธฐ์กด MulteFire๊ฐ€ ๊ฑฐ์˜ ์ œ๋กœ ์ฒ˜๋ฆฌ๋Ÿ‰ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๊ฒฝ์šฐ ๋†’์€ ์ฒ˜๋ฆฌ๋Ÿ‰ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ์š”์•ฝํ•˜๋ฉด ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ ์…€๋ฃฐ๋Ÿฌ ํ†ต์‹ ์˜ ์„ฑ๋Šฅ์„ ๋ถ„์„ ํ•ฉ๋‹ˆ๋‹ค. ์ œ์•ˆ ๋œ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ์šฐ๋ฆฌ๋Š” ๋ ˆ๊ฑฐ์‹œ ๋‹ค์ค‘ ๋ฐ˜์†กํŒŒ ๋™์ž‘์„ ์ฃผ์žฅํ•˜๋ฉฐ ๋น„๋ฉดํ—ˆ ์…€๋ฃฐ๋Ÿฌ ํ†ต์‹ ์˜ HARQ๋Š” ํšจ์œจ์ ์ด์ง€ ์•Š๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ, ์šฐ๋ฆฌ๋Š” ์ตœ์ฒจ๋‹จ ๊ธฐ ์ˆ ์— ๋น„ํ•ด UPT ๋ฐ ์ฒ˜๋ฆฌ๋Ÿ‰๊ณผ ๊ฐ™์€ ๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ๋‹ฌ์„ฑํ•˜๋Š” OOBE ์ธ์‹ ์ถ”๊ฐ€ ์•ก์„ธ์Šค ๋ฐ W-ARQ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค.3GPP has developed 5 GHz unlicensed band LTE, referred to as licensed-assisted access (LAA). LAA adopts listen before talk (LBT) operation, resembling Wi-Fis carrier sense multiple access with collision avoidance (CSMA/CA), to enable collision avoidance capability, while the frame structure overhead of each LAA downlink burst varies with the ending time of each preceding LBT operation. In this dissertation, we propose numerical model to analyze unlicensed band cellular communication. Next, we consider the following two enhancements of unlicensed band cellular communication: (i) out-of-band emission (OOBE) aware additional carrier access, and (ii) Wi-Fi assisted hybrid automatic repeat request (H-ARQ) for unlicensed-band stand-alone cellular communication. Given that, existing analytic models of Wi-Fi cannot be used to evaluate the performance of LAA, in this letter, we propose a novel Markov chain-based analytic model to analyze the performance of LAA network composed of multiple contending evolved NodeBs by considering the variation of the LAA frame structure overhead. LTE-LAA adopts adaptive modulation and coding (AMC) for the rate adaptation algorithm inherited from LTE. AMC helps the evolved nodeB (eNB) to select a modulation and coding scheme (MCS) for the next transmission using the channel quality indicator feedback of the current transmission. For the conventional LTE operating in the licensed band, there is no node contention problem and AMC performance has been well studied. However, in the case of LTE-LAA operating in the unlicensed band, AMC performance has not been properly addressed due to the collision problem. In this letter, we propose a novel Markov chain-based analysis model for analyzing LTELAA performance under a realistic channel model considering AMC operation. We adopt Rayleigh fading channel model widely used in wireless network analysis, and compare our analysis results with the results obtained from ns-3 simulator. Comparison results show an average accuracy of 99.5%, which demonstrates the accuracy of our analysis model. Due to the requirement for a high data rate, the 3GPP has provided multi-carrier operation for LTE-LAA. However, multi-carrier operation is susceptible to OOBE and uses limited transmission power, resulting in inefficient channel usage. This paper proposes a novel multi-carrier access scheme to enhance channel efficiency. Our proposed scheme divides a transmission burst into multiple ones and uses short subframe transmission while meeting the transmission power limitation. In addition, we propose an energy detection algorithm to overcome the OOBE problem by deciding the channel status accurately. Our prototype using software-defined radio shows the feasibility and performance of the energy detection algorithm that determines the channel status with over 99% accuracy. Through ns-3 simulation, we confirm that the proposed multi-carrier access scheme achieves up to 59% and 21.5% performance gain in userperceived throughput compared with the conventional LBT type A and type B, respectively. Since the legacy LAA has deployment problem, 3GPP and MulteFire alliance proposed unlicensed band stand-alone cellular communication system. However, conventional unlicensed band stand-alone cellular communication system has low transmission probability of uplink control messages. This disertation proposes W-ARQ: Wi-Fi assisted HARQ which put uplink control messages into Wi-Fi block ACK frame. In addition we propose parallel HARQ and clustered Minstrel to enhance throughput performance of W-ARQ. Our proposed algorithm shows high throughput performance where conventional MulteFire shows almost zero throughput performance. In summary, we analyze the performance of unlicensed-band cellular communication. By using the proposed model, we insist the legacy multi-carrier operation and HARQ of unlicensed cellular communication is not efficient. By this reason, we propose OOBE aware additional access and W-ARQ which achievee enhancements of network performance such as UPT and throughput compared with state-of-the-art techniques.Abstract i Contents iv List of Tables vii List of Figures viii 1 Introduction 1 1.1 Unlicensed Band Communication System . . . . . . . . . . . . . . . 1 1.2 Overview of Existing Approaches . . . . . . . . . . . . . . . . . . . 2 1.2.1 License-assisted access . . . . . . . . . . . . . . . . . . . . . 2 1.2.2 Further LAA . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3 Non-3GPP Unlicensed Band Cellular Communication . . . . 6 1.3 Main Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.1 Performance Analysis of LTE-LAA . . . . . . . . . . . . . . 6 1.3.2 Out-of-Band Emission Aware Additional Carrier Access for LTE-LAA Network . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.3 W-ARQ: Wi-Fi Assisted HARQ for Unlicensed Band StandAlone Cellular Communication System . . . . . . . . . . . . 8 1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . 8 2 Performance Analysis of LTE-LAA network 10 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Proposed Markov-Chain Model . . . . . . . . . . . . . . . . . . . . . 14 2.3.1 Markov Property . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.2 Markov Chain Model for EPS Type Variation . . . . . . . . . 16 2.3.3 LAA Network Throughput Estimation . . . . . . . . . . . . . 18 2.4 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3 Out-of-Band Emission Aware Additional Carrier Access for LTE-LAA Network 35 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Related work and Background . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.2 Listen Before Talk . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.3 Out-of-Band Emission . . . . . . . . . . . . . . . . . . . . . 39 3.3 Multi-carrier Operation of LTE-LAA . . . . . . . . . . . . . . . . . . 39 3.4 Carrier Sensing considering Out-of-Band Emission . . . . . . . . . . 47 3.4.1 Energy Detection Algorithm . . . . . . . . . . . . . . . . . . 49 3.4.2 Nominal Band Energy Detection . . . . . . . . . . . . . . . . 50 3.4.3 OOBE-Free Region Energy Detection . . . . . . . . . . . . . 51 3.5 Additional Carrier Access Scheme . . . . . . . . . . . . . . . . . . . 52 3.5.1 Basic Operation . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.5.2 Transmission Power Limitation . . . . . . . . . . . . . . . . 53 3.5.3 Dividing Transmission Burst . . . . . . . . . . . . . . . . . . 54 3.5.4 Short Subframe Decision . . . . . . . . . . . . . . . . . . . . 54 3.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.6.1 Performance of Energy Detection considering OOBE . . . . . 57 3.6.2 Simulation Environments . . . . . . . . . . . . . . . . . . . . 57 3.6.3 Performance of Proposed Carrier Access Scheme . . . . . . . 58 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4 W-ARQ: Wi-Fi Assisted HARQ for Unlicensed Band Stand-Alone Cellular Communication System 66 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4 W-ARQ: Wi-Fi assisted HARQ for Unlicensed Band Stand-Alone Cellular Communication System . . . . . . . . . . . . . . . . . . . . . . 69 4.4.1 Parallel HARQ . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.4.2 Clustered Minstrel . . . . . . . . . . . . . . . . . . . . . . . 72 4.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Concluding Remarks 80 5.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Abstract (In Korean) 90 ๊ฐ์‚ฌ์˜ ๊ธ€ 93Docto

    ASPIS: A Holistic and Practical Mechanism for Efficient MTC Support over Mobile Networks

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    Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

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    The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
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