230 research outputs found

    A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks

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    Massive MIMO is a promising technology to connect very large numbers of energy constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes in groups that can communicate simultaneously such that the mutual interference is minimized. We here propose node partitioning strategies that do not require full channel state information, but rather are based on nodes' respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency

    Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

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    Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to satisfy the high requirements of wireless aerial networks, several multiple access techniques have been investigated. In particular, space-division multiple access(SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink. Nevertheless, these gains are limited as they depend on the conditions of the environment. Hence, a generalized scheme has been recently proposed, called rate-splitting multiple access (RSMA), which is capable of achieving better spectral efficiency gains compared to SDMA and NOMA. In this paper, we present a comprehensive survey of key multiple access technologies adopted for aerial networks, where aBSs are deployed to serve ground users. Since there have been only sporadic results reported on the use of RSMA in aerial systems, we aim to extend the discussion on this topic by modelling and analyzing the weighted sum-rate performance of a two-user downlink network served by an RSMA-based aBS. Finally, related open issues and future research directions are exposed.Comment: 16 pages, 6 figures, submitted to IEEE Journa

    ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ํ™˜๊ฒฝ์—์„œ ๋‚ฎ์€ ๋ณต์žก๋„์˜ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ์‹ ํ˜ธ์ „์†ก์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ์ด์šฉํ™˜.Advanced wireless communication systems may employ massive multi-input multi-output (m-MIMO) techniques for performance improvement. A base station equipped with an m-MIMO configuration can serve a large number of users by means of beamforming. The m-MIMO channel becomes asymptotically orthogonal to each other as the number of antennas increases to infinity. In this case, we may optimally transmit signal by means of maximum ratio transmission (MRT) with affordable implementation complexity. However, the MRT may suffer from inter-user interference in practical m-MIMO environments mainly due to the presence of insufficient channel orthogonality. The use of zero-forcing beamforming can be a practical choice in m-MIMO environments since it can easily null out inter-user interference. However, it may require huge computational complexity for the generation of beam weight. Moreover, it may suffer from performance loss associated with the interference nulling, referred to transmission performance loss (TPL). The TPL may become serious when the number of users increases or the channel correlation increases in spatial domain. In this dissertation, we consider complexity-reduced multi-user signal transmission in m-MIMO environments. We determine the beam weight to maximize the signal-to-leakage plus noise ratio (SLNR) instead of signal-to-interference plus noise ratio (SINR). We determine the beam direction assuming combined use of MRT and partial ZF that partially nulls out interference. For further reduction of computational complexity, we determine the beam weight based on the approximated SLNR. We consider complexity-reduced ZF beamforming that generates the beam weight in a group-wise manner. We partition users into a number of groups so that users in each group experience low TPL. We approximately estimate the TPL for further reduction of computational complexity. Finally, we determine the beam weight for each user group based on the approximated TPL.์ฐจ์„ธ๋Œ€ ๋ฌด์„  ํ†ต์‹  ์‹œ์Šคํ…œ์—์„œ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ (massive MIMO) ๊ธฐ์ˆ ๋“ค์„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋Œ€๊ทœ๋ชจ ์•ˆํ…Œ๋‚˜๋ฅผ ๊ฐ€์ง„ ๊ธฐ์ง€๊ตญ์€ ๋งŽ์€ ์ˆ˜์˜ ์‚ฌ์šฉ์ž๋“ค์„ ๋น”ํฌ๋ฐ (beamforming)์œผ๋กœ ์„œ๋น„์Šคํ•ด์ค„ ์ˆ˜ ์žˆ๋‹ค. ์•ˆํ…Œ๋‚˜ ์ˆ˜๊ฐ€ ๋ฌดํ•œํžˆ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ์„œ ์ฑ„๋„์€ ์ ๊ทผ์ ์œผ๋กœ ์„œ๋กœ ์ง๊ต (orthogonal)ํ•˜๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฝ์šฐ, ๋‚ฎ์€ ์‹ค์žฅ ๋ณต์žก๋„๋ฅผ ๊ฐ€์ง€๋Š” ์ตœ๋Œ€ ๋น„ ์ „์†ก (maximum ratio transmission)์„ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ์‹ ํ˜ธ์ „์†ก์„ ์ตœ์ ํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ํ˜„์‹ค์ ์ธ ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ํ™˜๊ฒฝ์—์„œ๋Š” ์ฑ„๋„ ์ง๊ต์„ฑ์ด ์ถฉ๋ถ„ํ•˜์ง€ ๋ชปํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ตœ๋Œ€ ๋น„ ์ „์†ก์€ ๊ฐ„์„ญ์— ์˜ํ•œ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ๊ฒช์„ ์ˆ˜ ์žˆ๋‹ค. ์ œ๋กœ-ํฌ์‹ฑ (zero-forcing) ๋น”ํฌ๋ฐ์€ ๊ฐ„์„ญ์„ ์‰ฝ๊ฒŒ ์ œ๊ฑฐํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ํ™˜๊ฒฝ์—์„œ ํ˜„์‹ค์ ์ธ ์„ ํƒ์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ œ๋กœ-ํฌ์‹ฑ์€ ๋น” ๊ฐ€์ค‘์น˜ (beam weight) ์ƒ์„ฑ์œผ๋กœ ์ธํ•ด ๋†’์€ ๊ณ„์‚ฐ ๋ณต์žก๋„๋ฅผ ์š”๊ตฌํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์ œ๋กœ-ํฌ์‹ฑ์€ ๊ฐ„์„ญ ์ œ๊ฑฐ์— ๋Œ€ํ•œ ๋Œ€๊ฐ€๋กœ ์‹ฌ๊ฐํ•œ ์„ฑ๋Šฅ ์ €ํ•˜ (์ฆ‰, transmission performance loss; TPL)๋ฅผ ๊ฒช์„ ์ˆ˜ ์žˆ๋‹ค. TPL์€ ์‚ฌ์šฉ์ž ์ˆ˜๊ฐ€ ๋งŽ๊ฑฐ๋‚˜ ์ฑ„๋„์˜ ๊ณต๊ฐ„ ์ƒ๊ด€๋„๊ฐ€ ํด ๋•Œ ๋” ์‹ฌ๊ฐํ•ด์งˆ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ํ™˜๊ฒฝ์—์„œ ๋‚ฎ์€ ๋ณต์žก๋„์˜ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ์‹ ํ˜ธ์ „์†ก์„ ๊ณ ๋ คํ•œ๋‹ค. ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ์‹ ํ˜ธ-๋Œ€-๊ฐ„์„ญ ๋ฐ ์žก์Œ ๋น„ (signal-to-interference plus noise ratio) ๋Œ€์‹  ์‹ ํ˜ธ-๋Œ€-์œ ์ถœ ๋ฐ ์žก์Œ ๋น„ (signal-to-leakage plus noise ratio)๋ฅผ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ๋น” ๊ฐ€์ค‘์น˜๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ์ตœ๋Œ€ ๋น„ ์ „์†ก๊ณผ ๊ฐ„์„ญ์„ ์„ ํƒ์ ์œผ๋กœ ์ œ๊ฑฐํ•˜๋Š” ๋ถ€๋ถ„ ์ œ๋กœ-ํฌ์‹ฑ (partial zero-forcing)์˜ ์‚ฌ์šฉ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋น” ๋ฐฉํ–ฅ์„ ๊ฒฐ์ •ํ•œ๋‹ค. ๊ณ„์‚ฐ ๋ณต์žก๋„๋ฅผ ๋” ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ, ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ๊ทผ์‚ฌํ™”๋œ ์‹ ํ˜ธ-๋Œ€-์œ ์ถœ ๋ฐ ์žก์Œ๋น„๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋น” ๊ฐ€์ค‘์น˜๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ๊ทธ๋ฃน ๊ธฐ๋ฐ˜์œผ๋กœ ๋น” ๊ฐ€์ค‘์น˜๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋‚ฎ์€ ๋ณต์žก๋„์˜ ์ œ๋กœ-ํฌ์‹ฑ ๋น”ํฌ๋ฐ ์ „์†ก์„ ๊ณ ๋ คํ•œ๋‹ค. ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ์‚ฌ์šฉ์ž๋“ค์ด ๋‚ฎ์€ TPL์„ ๊ฐ–๋„๋ก ์‚ฌ์šฉ์ž๋“ค์„ ๋‹ค์ˆ˜์˜ ๊ทธ๋ฃน์œผ๋กœ ๋ถ„๋ฆฌ์‹œํ‚จ๋‹ค. ๊ณ„์‚ฐ ๋ณต์žก๋„๋ฅผ ๋” ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ, ์ œ์•ˆ ๊ธฐ๋ฒ•์€ TPL์„ ๊ทผ์‚ฌ์ ์œผ๋กœ ์ถ”์ •ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ๊ทผ์‚ฌํ™”๋œ TPL์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ˜•์„ฑ๋œ ๊ฐ ์‚ฌ์šฉ์ž ๊ทธ๋ฃน์— ๋Œ€ํ•˜์—ฌ ๋น” ๊ฐ€์ค‘์น˜๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค.Chapter 1. Introduction 1 Chapter 2. System model 10 Chapter 3. Complexity-reduced multi-user signal transmission 15 3.1. Previous works 15 3.2. Proposed scheme 24 3.3. Performance evaluation 47 Chapter 4. User grouping-based ZF transmission 57 4.1. Spatially correlated channel 57 4.2. Previous works 59 4.3. Proposed scheme 66 4.4. Performance evaluation 87 Chapter 5. Conclusions and further research issues 94 Appendix 97 A. Proof of Lemma 3-4 97 B. Proof of Lemma 3-5 100 C. Proof of strict quasi-concavity of SLNR_(k) 101 References 103 Korean Abstract 115Docto
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