400 research outputs found

    RCFD: A Novel Channel Access Scheme for Full-Duplex Wireless Networks Based on Contention in Time and Frequency Domains

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    In the last years, the advancements in signal processing and integrated circuits technology allowed several research groups to develop working prototypes of in-band full-duplex wireless systems. The introduction of such a revolutionary concept is promising in terms of increasing network performance, but at the same time poses several new challenges, especially at the MAC layer. Consequently, innovative channel access strategies are needed to exploit the opportunities provided by full-duplex while dealing with the increased complexity derived from its adoption. In this direction, this paper proposes RTS/CTS in the Frequency Domain (RCFD), a MAC layer scheme for full-duplex ad hoc wireless networks, based on the idea of time-frequency channel contention. According to this approach, different OFDM subcarriers are used to coordinate how nodes access the shared medium. The proposed scheme leads to efficient transmission scheduling with the result of avoiding collisions and exploiting full-duplex opportunities. The considerable performance improvements with respect to standard and state-of-the-art MAC protocols for wireless networks are highlighted through both theoretical analysis and network simulations.Comment: Submitted at IEEE Transactions on Mobile Computing. arXiv admin note: text overlap with arXiv:1605.0971

    Random Access in Massive MIMO by Exploiting Timing Offsets and Excess Antennas

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    Massive MIMO systems, where base stations are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of user equipments (UEs) increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this paper, a random access protocol is proposed that resolves collisions and performs timing estimation by simply utilizing the large number of antennas envisioned in Massive MIMO networks. UEs entering the network perform spreading in both time and frequency domains, and their timing offsets are estimated at the base station in closed-form using a subspace decomposition approach. This information is used to compute channel estimates that are subsequently employed by the base station to communicate with the detected UEs. The favorable propagation conditions of Massive MIMO suppress interference among UEs whereas the inherent timing misalignments improve the detection capabilities of the protocol. Numerical results are used to validate the performance of the proposed procedure in cellular networks under uncorrelated and correlated fading channels. With 2.5ร—1032.5\times10^3 UEs that may simultaneously become active with probability 1\% and a total of 1616 frequency-time codes (in a given random access block), it turns out that, with 100100 antennas, the proposed procedure successfully detects a given UE with probability 75\% while providing reliable timing estimates.Comment: 30 pages, 6 figures, 1 table, submitted to Transactions on Communication

    RCFD: A frequency-based channel access scheme for full-duplex wireless networks

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    Recently, several working implementations of inband full-duplex wireless systems have been presented, where the same node can transmit and receive simultaneously in the same frequency band. The introduction of such a possibility at the physical layer could lead to improved performance but also poses several challenges at the MAC layer. In this paper, an innovative mechanism of channel contention in full-duplex OFDM wireless networks is proposed. This strategy is able to ensure efficient transmission scheduling with the result of avoiding collisions and effectively exploiting full-duplex opportunities. As a consequence, considerable performance improvements are observed with respect to standard and state-of-the-art MAC protocols for wireless networks, as highlighted by extensive simulations performed in ad hoc wireless networks with varying number of nodes

    Frequency Domain Backoff for Continuous Beamforming Space Division Multiple Access on Massive MIMO Wireless Backhaul Systems

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    This paper newly proposes a frequency domain backoff scheme dedicated to continuous beamforming space division multiple access (CB-SDMA) on massive antenna systems for wireless entrance (MAS-WE). The entrance base station (EBS) has individual base band signal processing units for respective relay stations (RSs) to be accommodated. EBS then continuously applies beamforming weight to transmission/reception signals. CB-SDMA yields virtual point-to-point backhaul link where radio resource control messages and complicated multiuser scheduling are not required. This simplified structure allows RSs to work in a distributed manner. However, one issue remains to be resolved; overloaded multiple access resulting in collision due to its random access nature. The frequency domain backoff mechanism is introduced instead of the time domain one. It can flexibly avoid co-channel interference caused by excessive spatial multiplexing. Computer simulation verifies its superiority in terms of system throughput and packet delay

    5G ์ดํ›„ ๋ฌด์„  ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ๋ฌด์„  ์ ‘์† ๊ธฐ์ˆ  ํ–ฅ์ƒ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ๋ฐ•์„ธ์›….Recently, operators are creating services using 5G systems in various fields, e.g., manufacturing, automotive, health care, etc. 5G use cases include transmission of small packets using IoT devices to high data rate transmission such as high-definition video streaming. When a large-scale IoT device transmits a small packet, power saving is important, so it is necessary to disconnect from the base station and then establish a connection through random access to transmit data. However, existing random access procedures are difficult to satisfy various latency requirements. It is attractive to use a wide bandwidth of the millimeter wave spectrum for high data rate transmission. In order to overcome the channel characteristics, beamforming technology is applied. However, when determining a beam pair between a transmitter and a receiver, interference is not considered. In this dissertation, we consider the following three enhancements to enable 5G and beyond use cases: (i) Two-step random access procedure for delay-sensitive devices, (ii) self-uplink synchronization framework for solving preamble collision problem, and (iii) interference-aware beam adjustment for interference coordination. First, RAPID, two-step random access for delay-sensitive devices, is proposed to reduce latency requirement value for satisfying specific reliability. When devices, performing RAPID and contention-based random access, coexist, it is important to determine a value that is the number of preambles for RAPID to reduce random access load. Simulation results show that RAPID achieves 99.999% reliability with 80.8% shorter uplink latency, and also decreases random access load by 30.5% compared with state-of-the-art techniques. Second, in order to solve preamble collision problem, we develop self-uplink synchronization framework called EsTA. Preamble collision occurs when multiple devices transmit the same preamble. Specifically, we propose a framework that helps the UE to estimate the timing advance command using a deep neural network model and to determine the TA value. Estimation accuracy can achieve 98โ€“99% when subcarrier spacing is 30 and 60 kHz. Finally, we propose IBA, which is interference-aware beam adjustment method to reduce interference in millimeter wave networks. Unlike existing methods of reducing interference by scheduling time and frequency resources differently, interference is controlled through beam adjustment. In IBA, it is important to reduce search space of finding new beam pair to reduce interference. In practical, it is impossible to search beam pair of all combinations. Therefore, through Monte Carlo method, we can reduce search space to achieve local optimum. IBA achieve enhancement of lower 50%throughput up to 50% compared with only applying beam adjustment. In summary, we propose a two-step random access, a self-uplink synchronization framework, and interference-aware beam adjustment for 5G and beyond use cases. Through these researches, we achieve enhancements of network performance such as latency and throughput compared with state-of-the-art techniques.์ตœ๊ทผ ์‚ฌ์—…์ž๋Š” ์ œ์กฐ, ์ž๋™์ฐจ, ํ—ฌ์Šค ์ผ€์–ด ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ 5G ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•˜์—ฌ ์„œ๋น„์Šค๋ฅผ ๋งŒ๋“ค๊ณ  ์žˆ๋‹ค. 5G ์‚ฌ์šฉ ์‚ฌ๋ก€์—๋Š” IoT ์žฅ์น˜๋ฅผ ์ด์šฉํ•œ ์ž‘์€ ํŒจํ‚ท ์ „์†ก์—์„œ๊ณ ํ™”์งˆ ๋น„๋””์˜ค ์ŠคํŠธ๋ฆฌ๋ฐ๊ณผ ๊ฐ™์€ ๊ณ ์† ๋ฐ์ดํ„ฐ ์ „์†ก๊นŒ์ง€ ํฌํ•จ๋œ๋‹ค. ๋Œ€๊ทœ๋ชจ IoT ์žฅ์น˜๊ฐ€์ž‘์€ ํŒจํ‚ท์„ ์ „์†กํ•˜๋Š” ๊ฒฝ์šฐ ์ „๋ ฅ ์†Œ๋ชจ ์ ˆ์•ฝ์ด ์ค‘์š”ํ•˜๋ฏ€๋กœ ๊ธฐ์ง€๊ตญ๊ณผ์˜ ์—ฐ๊ฒฐ์„ ๋Š์€๋‹ค์Œ ๋žœ๋ค ์•ก์„ธ์Šค๋ฅผ ํ†ตํ•ด ๋‹ค์‹œ ๊ธฐ์ง€๊ตญ๊ณผ ์—ฐ๊ฒฐํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์†กํ•ด์•ผํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜๊ธฐ์กด์˜ ๋žœ๋ค ์•ก์„ธ์Šค ์ ˆ์ฐจ๋Š” ๋‹ค์–‘ํ•œ ์ง€์—ฐ์‹œ๊ฐ„ ์š”๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์–ด๋ ต๋‹ค. ํ•œํŽธ, ๋†’์€๋ฐ์ดํ„ฐ ์ „์†ก ์†๋„๋ฅผ ์œ„ํ•ด ๋„“์€ ๋Œ€์—ญํญ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋Œ€์—ญ์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ด๋•Œ, ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋Œ€์—ญ ์ฑ„๋„ ํŠน์„ฑ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๋น”ํฌ๋ฐ ๊ธฐ์ˆ ์ด ์ ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฌ 5Gํ‘œ์ค€์—์„œ ์†ก์‹ ๊ธฐ์™€ ์ˆ˜์‹ ๊ธฐ ์‚ฌ์ด์˜ ๋น” ์Œ์„ ๊ฒฐ์ •ํ•  ๋•Œ, ๊ฐ„์„ญ์€ ๊ณ ๋ ค๋˜์ง€ ์•Š๋Š”๋‹ค. ์ด๋…ผ๋ฌธ์—์„œ๋Š” 5G ๋ฐ ๊ทธ ์ดํ›„์˜ ๋„คํŠธ์›Œํฌ์—์„œ ๋‹ค์–‘ํ•œ ์‚ฌ์šฉ ์‚ฌ๋ก€๋ฅผ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์Œ์„ธ ๊ฐ€์ง€ ๊ฐœ์„  ์‚ฌํ•ญ์„ ๊ณ ๋ คํ•œ๋‹ค. (i) ์ง€์—ฐ์— ๋ฏผ๊ฐํ•œ ์žฅ์น˜๋ฅผ ์œ„ํ•œ 2 ๋‹จ๊ณ„ ๋žœ๋ค ์•ก์„ธ์Šค์ ˆ์ฐจ, (ii) ํ”„๋ฆฌ์•ฐ๋ธ” ์ถฉ๋Œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ž์ฒด ์ƒํ–ฅ๋งํฌ ๋™๊ธฐํ™” ํ”„๋ ˆ์ž„ ์›Œํฌ,๊ทธ๋ฆฌ๊ณ  (iii) ๊ฐ„์„ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๊ฐ„์„ญ ์ธ์‹ ๋น” ์กฐ์ •์ด๋‹ค. ์ฒซ์งธ, ์ง€์—ฐ์— ๋ฏผ๊ฐํ•œ ์žฅ์น˜๋ฅผ ์œ„ํ•œ 2 ๋‹จ๊ณ„ ๋žœ๋ค ์•ก์„ธ์Šค์ธ RAPID๋Š” ํŠน์ • ์‹ ๋ขฐ๋„๋ฅผ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ง€์—ฐ์‹œ๊ฐ„์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. RAPID์™€ ๊ฒฝํ•ฉ ๊ธฐ๋ฐ˜ ๋žœ๋ค ์•ก์„ธ์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ์žฅ์น˜๊ฐ€ ๊ณต์กดํ•  ๊ฒฝ์šฐ RAPID๊ฐ€ ๋žœ๋ค ์•ก์„ธ์Šค ๋ถ€ํ•˜๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด RAPID๋ฅผ ์œ„ํ•ด ํ• ๋‹น๋˜๋Š” ํ”„๋ฆฌ์•ฐ๋ธ” ์ˆ˜๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด RAPID๋Š” 99.999%์˜์‹ ๋ขฐ๋„๋ฅผ ๋งŒ์กฑ์‹œํ‚ค๋Š” ์ง€์—ฐ์‹œ๊ฐ„์„ ์ตœ์‹  ๊ธฐ์ˆ ์— ๋น„ํ•ด 80.8% ์ค„์ด๋ฉด์„œ, ๋žœ๋ค ์•ก์„ธ์Šค๋ถ€ํ•˜๋ฅผ 30.5% ์ค„์ธ๋‹ค. ๋‘˜์งธ, ํ”„๋ฆฌ์•ฐ๋ธ” ์ถฉ๋Œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ž์ฒด ์ƒํ–ฅ๋งํฌ ๋™๊ธฐํ™” ํ”„๋ ˆ์ž„์›Œํฌ์ธ EsTA๋ฅผ ๊ฐœ๋ฐœํ•œ๋‹ค. ํ”„๋ฆฌ์•ฐ๋ธ” ์ถฉ๋Œ์€ ์—ฌ๋Ÿฌ ์žฅ์น˜๊ฐ€ ๋™์ผํ•œ ํ”„๋ฆฌ์•ฐ๋ธ”์„ ์ „์†กํ•  ๋•Œ ๋ฐœ์ƒํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋‹จ๋ง์ด ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ timing advance(TA) command๋ฅผ ์ถ”์ •ํ•˜๊ณ  TA๊ฐ’์„ ๊ฒฐ์ •ํ•˜๋Š” ํ”„๋ ˆ์ž„ ์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋„คํŠธ์›Œํฌ ์‹œ์Šคํ…œ์˜ ๋ถ€๋ฐ˜์†กํŒŒ ๊ฐ„๊ฒฉ์ด 30 ๋ฐ 60 kHz ์ผ ๋•Œ, TA command ์ถ”์ • ์ •ํ™•๋„๋Š”98โ€“99%๋ฅผ ๋‹ฌ์„ฑ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋„คํŠธ์›Œํฌ์—์„œ ๊ฐ„์„ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๊ฐ„์„ญ ์ธ์‹ ๋น” ์กฐ์ • ๋ฐฉ๋ฒ•์ธ IBA๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๊ฐ„๊ณผ ์ฃผํŒŒ์ˆ˜ ์ž์›์„ ๋‹ค๋ฅด๊ฒŒ ์˜ˆ์•ฝํ•˜์—ฌ ๊ฐ„์„ญ์„ ์ค„์ด๋Š” ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๊ณผ ๋‹ฌ๋ฆฌ IBA๋Š” ๋น” ์กฐ์ •์„ ํ†ตํ•ด ๊ฐ„์„ญ์„ ์ œ์–ดํ•œ๋‹ค.์ด ๋•Œ, ๊ฐ„์„ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ๋น” ์Œ์„ ์ฐพ๋Š” ๊ฒ€์ƒ‰ ๊ณต๊ฐ„์„ ์ค„์ด๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค.ํ˜„์‹ค์ ์œผ๋กœ ๋ชจ๋“  ๋น” ์Œ์˜ ์กฐํ•ฉ์„ ๊ฒ€์ƒ‰ํ•˜๋Š” ๊ฒƒ์€ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๋”ฐ๋ผ์„œ IBA๋Š” Monte Carlo ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๊ฒ€์ƒ‰ ๊ณต๊ฐ„์„ ์ถ•์†Œํ•˜์—ฌ local optimum์„ ๋‹ฌ์„ฑํ•˜๋„๋ก ์„ค๊ณ„๋˜์–ด์•ผํ•œ๋‹ค. IBA๋Š” 5G ํ‘œ์ค€์˜ ๋น” ์กฐ์ • ๋ฐฉ๋ฒ•๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ํ•˜์œ„ 50% throughput์˜ ์ค‘๊ฐ„๊ฐ’์ด์ตœ๋Œ€ 50%๊นŒ์ง€ ํ–ฅ์ƒ๋œ๋‹ค. ์š”์•ฝํ•˜๋ฉด, ์šฐ๋ฆฌ๋Š” 5G ๋ฐ ๊ทธ ์ดํ›„์˜ ๋‹ค์–‘ํ•œ ์‚ฌ์šฉ ์‚ฌ๋ก€๋ฅผ ์œ„ํ•ด์„œ 2 ๋‹จ๊ณ„ ๋žœ๋ค ์•ก์„ธ์Šค, ์ž์ฒด ์ƒํ–ฅ๋งํฌ ๋™๊ธฐํ™” ํ”„๋ ˆ์ž„ ์›Œํฌ, ๊ทธ๋ฆฌ๊ณ  ๊ฐ„์„ญ ์ธ์‹ ๋น”์กฐ์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ตœ์‹  ๊ธฐ์ˆ ์— ๋น„ํ•ด ์ง€์—ฐ์‹œ๊ฐ„ ๋ฐ ์ฒ˜๋ฆฌ๋Ÿ‰๊ณผ ๊ฐ™์€๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋œ๋‹ค.1 Introduction 1 1.1 5G Vision, Applications, and Keywords 1 1.2 Overview of Existing Approach 3 1.3 Main Contributions 4 1.3.1 RAPID: Two-Step Random Access 4 1.3.2 EsTA: Self-Uplink Synchronization 5 1.3.3 IBA: Interference-Aware Beam Adjustment 5 1.4 Organization of the Dissertation 6 2 RAPID: Contention Resolution-based Random Access Procedure using Context ID for IoT 7 2.1 Introduction 7 2.2 Background 10 2.2.1 RRC State 10 2.2.2 Random Access Procedure 11 2.2.3 Uplink Latency in RRC INACTIVE State 13 2.2.4 Related Work 14 2.3 RAPID: Proposed Random Access Procedure 15 2.3.1 Overview 15 2.3.2 Criterion of Applying RAPID 16 2.3.3 Preamble Set and RACH Period Allocation 17 2.3.4 Preamble Transmission 18 2.3.5 RAR Transmission 19 2.3.6 AS Context ID Allocation 21 2.3.7 Number of Preambles for RAPID 22 2.4 Access Pattern Analyzer 22 2.4.1 Overview 22 2.4.2 APA Operation 23 2.4.3 Margin Value 26 2.4.4 Offset Index Decision 26 2.5 Random Access Load Analysis 27 2.5.1 System Model 28 2.5.2 Markov Chain Model for 4-Step RA 29 2.5.3 Average Random Access Load for 4-Step RA 34 2.5.4 Markov Chain Model for RAPID 34 2.5.5 Average Random Access Load for RAPID 37 2.5.6 Validation of Analysis 38 2.5.7 Optimization Problem 41 2.6 Performance Evaluation 42 2.6.1 Simulation Setup 42 2.6.2 Number of Preambles for RAPID 43 2.6.3 Performance of RAPID 43 2.6.4 Performance of APA 48 2.7 Summary 48 3 EsTA: Self-Uplink Synchronization in 2-Step Random Access 49 3.1 Introduction 49 3.2 Background 51 3.2.1 Overview of 2-Step CBRA 51 3.2.2 Channel Structure for msgA 52 3.2.3 TA Handling for the Payload 54 3.2.4 2-Step Random Access in Recent Literature 56 3.3 Challenges of 2-Step Random Access 57 3.3.1 Preamble Allocation 57 3.3.2 Resource Mapping for msgA 58 3.3.3 DFT Operation in gNB 58 3.3.4 Detected Collision Problem 58 3.4 EsTA: Proposed Self-UL Synchronization Procedure 59 3.4.1 Overview 60 3.4.2 Overall Procedures 60 3.4.3 Performance Evaluation 61 3.4.4 Future Research Perspectives 65 3.5 Summary 65 4 IBA: Interference-Aware Beam Adjustment for 5G mmWave Networks 67 4.1 Introduction 67 4.2 Background 68 4.2.1 Beam Management in 5G NR 68 4.2.2 System-Level Simulation and 3D Beamforming for 5G NR 70 4.3 Motivation 70 4.3.1 Throughput Degradation by Interference 70 4.4 IBA: Proposed Interference Management Scheme 72 4.4.1 Overall Procedure 72 4.4.2 Reduction of Search Space 72 4.4.3 Algorithm for IBA 75 4.5 Performance Evaluation 76 4.6 Summary 78 5 Concluding Remarks 79 5.1 Research Contributions 79 5.2 Future Work 80 Abstract (In Korean) 89 ๊ฐ์‚ฌ์˜ ๊ธ€ 92Docto

    Random Access in Uplink Massive MIMO Systems: How to exploit asynchronicity and excess antennas

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    Massive MIMO systems, where the base stations are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of users increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this work, we propose a random access procedure that resolves collisions and also performs timing, channel, and power estimation by simply utilizing the large number of antennas envisioned in massive MIMO systems and the inherent timing misalignments of uplink signals during network access and handover. Numerical results are used to validate the performance of the proposed solution under different settings. It turns out that the proposed solution can detect all collisions with a probability higher than 90%, at the same time providing reliable timing and channel estimates. Moreover, numerical results demonstrate that it is robust to overloaded situations.Comment: submitted to IEEE Globecom 2016, Washington, DC US
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