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    ๊ณ ์† ์ด๋™ ํ™˜๊ฒฝ์—์„œ ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ์ด์šฉํ™˜.Advanced cellular communication systems may obtain high array gain by employing massive multi-input multi-output (m-MIMO) systems, which may require accurate channel state information (CSI). When users are in high mobility, it may not be easy to get accurate CSI. When we transmit signal to users in high mobility, we may experience serious performance loss due to the inaccuracy of outdated CSI, associated with so-called channel aging effect. This problem may be alleviated by exploiting channel correlation matrix (CCM) in spatial domain. However, it may require an additional process for the estimation of CCM, which may require high signaling overhead in m-MIMO environments. In this dissertation, we consider signal transmission to multiple users in high mobility in m-MIMO environments. We consider the estimation of CSI with reduced signaling overhead. The signaling overhead for the CSI estimation is a challenging issue in m-MIMO environments. We may reduce the signaling overhead for the CSI estimation by using pilot signal transmitted by means of beamforming with a weight determined by eigenvectors of CCM. To this end, we need to estimate the CCM, which may still require large signaling overhead. We consider the estimation of CCM with antennas in a uniform linear array (ULA). Since pairs of antennas with an equal distance may experience spatial channel correlation similar to each other in ULA antenna environments, we may jointly estimate the spatial channel correlation. We estimate the mean-square error (MSE) of elements of estimated CCM and then discard the elements whose MSE is higher than a reference value for the improvement of CCM estimation. We may estimate the CSI from the estimated CCM with reduced signaling overhead. We consider signal transmission robust to the presence of channel aging effect. Users in different mobility may differently experience the channel aging effect. This means that they may differently suffer from transmission performance loss. To alleviate this problem, we transmit signal to maximize the average signal-to-leakage-plus-noise ratio, making it possible to individually handle the channel aging effect. We consider the signal transmission to the eigen-direction of a linear combination of CSI and CCM. Analyzing the transmission performance in terms of signal-to-interference-plus-noise ratio, we control the transmit power by using an iterative water-filling technique. Finally, we consider the allocation of transmission resource in the presence of channel aging effect. We design a sub-optimal greedy algorithm that allocates the transmission resource to maximize the sum-rate in the presence of channel aging effect. We may estimate the sum-rate from the beam weight and a hypergeometric function (HF) that represents the effect of outdated CSI on the transmission performance. However, it may require very high computational complexity to calculate the beam weight and the HF in m-MIMO environments. To alleviate the complexity problem, we determine the beam weight in dominant eigen-direction of CCM and approximate the HF as a function of temporal channel correlation. Since we may estimate the sum-rate by exploiting spatial and temporal channel correlation, we may need to update the resource allocation only when the change of CCM or temporal channel correlation is large enough to affect the sum-rate. Simulation results show that the proposed scheme provides performance similar to a greedy algorithm based on accurate sum-rate, while significantly reducing the computational complexity.๊ธฐ์ง€๊ตญ์ด ์ˆ˜๋งŽ์€ ์•ˆํ…Œ๋‚˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋†’์€ ์ „์†ก ์ด๋“์„ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜(massive MIMO) ์‹œ์Šคํ…œ์ด ์ฐจ์„ธ๋Œ€ ๋ฌด์„  ํ†ต์‹  ์‹œ์Šคํ…œ์œผ๋กœ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ •ํ™•ํ•œ ์ฑ„๋„ ์ •๋ณด(channel state information)๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์‹ ํ˜ธ ์ „์†ก ๋ฐ ์ž์› ๊ด€๋ฆฌ ๊ธฐ์ˆ ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ํ•˜์ง€๋งŒ ์‚ฌ์šฉ์ž๊ฐ€ ๊ณ ์†์œผ๋กœ ์ด๋™ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ๋Š” ๊ธฐ์ง€๊ตญ์ด ์ถ”์ •ํ•œ ์ฑ„๋„ ์ •๋ณด์™€ ์‹ค์ œ ์ „์†ก ์ฑ„๋„์ด ํฌ๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š” ์ฑ„๋„ ๋ณ€ํ™” ํšจ๊ณผ(channel aging effect)๊ฐ€ ๋ฐœ์ƒํ•˜์—ฌ, ์‹œ์Šคํ…œ ์ „์†ก ์„ฑ๋Šฅ์ด ์‹ฌ๊ฐํ•˜๊ฒŒ ํ•˜๋ฝํ•  ์ˆ˜ ์žˆ๋‹ค. ์œ„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ์ƒ๋Œ€์ ์œผ๋กœ ์‚ฌ์šฉ์ž ์ด๋™์„ฑ์— ๋Š๋ฆฌ๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š” ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ(channel correlation matrix)์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ๋Š” ๊ธฐ์ง€๊ตญ์ด ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ์„ ์ถ”์ •ํ•˜๋Š” ๊ณผ์ •์—์„œ ํฐ ํŒŒ์ผ๋Ÿฟ(pilot) ์‹ ํ˜ธ ๋ถ€๋‹ด์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๊ณ ์† ์ด๋™ ํ™˜๊ฒฝ์—์„œ์˜ ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž์— ๋Œ€ํ•œ ์‹ ํ˜ธ ์ „์†ก์„ ๊ณ ๋ คํ•œ๋‹ค. ์šฐ์„ , ๋‚ฎ์€ ํŒŒ์ผ๋Ÿฟ ์‹ ํ˜ธ ๋ถ€๋‹ด์„ ๊ฐ–๋Š” ์ฑ„๋„ ์ •๋ณด ์ถ”์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋Œ€๊ทœ๋ชจ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ ์ฑ„๋„ ์ •๋ณด ์ถ”์ •์€ ํฐ ํŒŒ์ผ๋Ÿฟ ์‹ ํ˜ธ ๋ถ€๋‹ด์„ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋•Œ ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ์„ ํ™œ์šฉํ•œ ํŒŒ์ผ๋Ÿฟ ์‹ ํ˜ธ ์„ค๊ณ„๋ฅผ ํ†ตํ•˜์—ฌ ์ฑ„๋„ ์ •๋ณด ์ถ”์ •์œผ๋กœ ์ธํ•œ ์‹ ํ˜ธ ๋ถ€๋‹ด์„ ํšจ๊ณผ์ ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ์„ ์ถ”์ •ํ•ด์•ผ ํ•˜๋ฉฐ, ์ด ๊ณผ์ •์—์„œ ํฐ ์‹ ํ˜ธ ๋ถ€๋‹ด์ด ์•ผ๊ธฐ๋  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ๊ธฐ์ง€๊ตญ์ด ๊ท ์ผํ•œ ์„ ํ˜• ์•ˆํ…Œ๋‚˜ ๋ฐฐ์—ด(uniform linear array)์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ํ™˜๊ฒฝ์—์„œ, ๊ฐ™์€ ๊ฑฐ๋ฆฌ์˜ ์•ˆํ…Œ๋‚˜ ์Œ๋“ค์˜ ์ฑ„๋„ ๊ฐ„ ๊ณต๊ฐ„ ์ƒ๊ด€๋„๊ฐ€ ์œ ์‚ฌํ•˜๋‹ค๋Š” ํŠน์ง•์„ ํ™œ์šฉํ•˜์—ฌ, ์ƒ๊ธฐ ์•ˆํ…Œ๋‚˜ ์Œ๋“ค์˜ ์ฑ„๋„ ๊ฐ„ ๊ณต๊ฐ„ ์ƒ๊ด€๋„๋ฅผ ์ตœ์†Œ์ž์Šน์ถ”์ •๋ฒ•(least-square estimation)์„ ํ™œ์šฉํ•˜์—ฌ ์ถ”์ •ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ถ”์ •๋œ ๊ณต๊ฐ„ ์ƒ๊ด€๋„์˜ ํ‰๊ท ์ œ๊ณฑ์˜ค์ฐจ(mean-square error)๋ฅผ ์ถ”์ •ํ•˜์—ฌ, ์ƒ๊ธฐ ํ‰๊ท ์ œ๊ณฑ์˜ค์ฐจ๊ฐ€ ํฐ ๊ณต๊ฐ„ ์ƒ๊ด€๋„๋ฅผ 0์œผ๋กœ ์น˜ํ™˜ํ•˜์—ฌ ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ์˜ ์ถ”์ • ์ •ํ™•๋„๋ฅผ ๋†’์ธ๋‹ค. ๋˜ํ•œ ์ƒ๊ธฐ ์ถ”์ •ํ•œ ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ์„ ํ™œ์šฉํ•˜์—ฌ ๋‚ฎ์€ ์‹ ํ˜ธ ๋ถ€๋‹ด์œผ๋กœ ์ฑ„๋„ ์ •๋ณด๋ฅผ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์„ ๋ณด์ธ๋‹ค. ๋‘˜์งธ๋กœ, ์‚ฌ์šฉ์ž ์ด๋™์„ฑ์— ์˜ํ•œ ์ฑ„๋„ ๋ณ€ํ™”์— ๊ฐ•์ธํ•œ ์‹ ํ˜ธ ์ „์†ก ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฌ์šฉ์ž๋“ค์ด ์„œ๋กœ ๋‹ค๋ฅธ ์†๋„๋กœ ์ด๋™ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ๋Š” ์ฑ„๋„ ๋ณ€ํ™”์— ์˜ํ•œ ์‹ ํ˜ธ ์ „์†ก ์„ฑ๋Šฅ ์ €ํ•˜ ์—ญ์‹œ ์‚ฌ์šฉ์ž๋งˆ๋‹ค ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ์œ„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ๊ฐ ์‚ฌ์šฉ์ž์— ๋Œ€ํ•œ ์ฑ„๋„ ๋ณ€ํ™” ํšจ๊ณผ๋ฅผ ๊ฐœ๋ณ„์ ์œผ๋กœ ๊ณ ๋ คํ•˜๋ฉด์„œ ํ‰๊ท  ์‹ ํ˜ธ ๋Œ€ ๋ˆ„์ˆ˜๊ฐ„์„ญ ๋ฐ ์žก์Œ๋น„(signal-to-leakage-plus-noise ratio)๋ฅผ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ์ „์†ก ๋น” ๊ฐ€์ค‘์น˜๋ฅผ ์„ค๊ณ„ํ•œ๋‹ค. ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ์‚ฌ์šฉ์ž๋“ค์˜ ์ฑ„๋„ ์ •๋ณด์™€ ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ์˜ ์„ ํ˜• ๊ฒฐํ•ฉ์˜ ๊ณ ์œ ๋ฐฉํ–ฅ(eigen-direction)์œผ๋กœ ์‹ ํ˜ธ๋ฅผ ์ „์†กํ•œ๋‹ค. ๋˜ํ•œ ์ œ์•ˆ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•  ๋•Œ์˜ ์‹ ํ˜ธ ๋Œ€ ๊ฐ„์„ญ ๋ฐ ์žก์Œ๋น„(signal-to-interference-plus-noise ratio)๋ฅผ ๋ถ„์„ํ•˜๊ณ , ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ „์†ก ์ „๋ ฅ ๋ถ„๋ฐฐ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋์œผ๋กœ, ์‚ฌ์šฉ์ž ์ด๋™์„ฑ์— ๋”ฐ๋ฅธ ์ฑ„๋„ ๋ณ€ํ™”๋ฅผ ๊ณ ๋ คํ•˜๋Š” ์ž์› ํ• ๋‹น ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ, ์ƒ๊ธฐ ์ฑ„๋„ ๋ณ€ํ™”๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์‹œ์Šคํ…œ ์ „์†ก ์„ฑ๋Šฅ(sum-rate)์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ํƒ์š•(greedy) ์•Œ๊ณ ๋ฆฌ๋“ฌ ๊ธฐ๋ฐ˜์˜ ์ž์› ํ• ๋‹น ๊ธฐ์ˆ ์„ ์„ค๊ณ„ํ•œ๋‹ค. ๊ณ ์† ์ด๋™ ํ™˜๊ฒฝ์—์„œ ์‹œ์Šคํ…œ ์ „์†ก ์„ฑ๋Šฅ์„ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‚ฌ์šฉ์ž๋“ค์— ๋Œ€ํ•œ ์ „์†ก ๋น” ๊ฐ€์ค‘์น˜์™€ ํ–‰๋ ฌ์— ๋Œ€ํ•œ ์ดˆ๊ธฐํ•˜ ํ•จ์ˆ˜(hypergeometric function of a matrix argument)์™€ ๊ด€๋ จ๋œ ๋ณต์žกํ•œ ์—ฐ์‚ฐ์ด ํ•„์š”ํ•˜๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ๋น” ๊ฐ€์ค‘์น˜๋ฅผ ๊ณต๊ฐ„ ์ƒ๊ด€๋„ ํ–‰๋ ฌ์˜ ๊ณ ์œ ๋ฐฉํ–ฅ์œผ๋กœ ๊ฒฐ์ •ํ•˜๊ณ , ์ดˆ๊ธฐํ•˜ ํ•จ์ˆ˜๋ฅผ ์ฑ„๋„ ์‹œ๊ฐ„ ์ƒ๊ด€๋„์— ๋Œ€ํ•œ ํ•จ์ˆ˜๋กœ ๊ทผ์‚ฌํ•œ๋‹ค. ์ƒ๊ธฐ ์ „์†ก ์„ฑ๋Šฅ ์ถ”์ • ๋ฐฉ๋ฒ•์ด ์ฑ„๋„์˜ ๊ณต๊ฐ„ ๋ฐ ์‹œ๊ฐ„ ์ƒ๊ด€๋„์—๋งŒ ์˜์กดํ•œ๋‹ค๋Š” ์ ์„ ํ™œ์šฉํ•˜์—ฌ, ์ฑ„๋„ ๊ณต๊ฐ„ ๋ฐ ์‹œ๊ฐ„ ์ƒ๊ด€๋„๊ฐ€ ํฌ๊ฒŒ ๋ณ€ํ™”ํ•œ ์‚ฌ์šฉ์ž๊ฐ€ ์กด์žฌํ•  ๋•Œ์— ํ•œํ•˜์—ฌ ์‚ฌ์šฉ์ž๋“ค์— ๋Œ€ํ•œ ์ž์› ํ• ๋‹น ์ƒํƒœ๋ฅผ ๊ฐฑ์‹ ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ, ์ œ์•ˆ ๊ธฐ๋ฒ•์ด ๋ณต์žกํ•œ ์‹œ์Šคํ…œ ์ „์†ก ์„ฑ๋Šฅ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ž์› ํ• ๋‹น ๋ฐฉ๋ฒ•๊ณผ ์œ ์‚ฌํ•œ ์ž์› ํ• ๋‹น ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉด์„œ๋„ ๊ณ„์‚ฐ ๋ณต์žก๋„๋ฅผ ํš๊ธฐ์ ์œผ๋กœ ์ค„์ด๋Š” ๊ฒƒ์„ ๋ณด์ธ๋‹ค.Abstract i Contents v List of Figures vii List of Tables ix Chapter 1. Introduction 1 Chapter 2. M-MIMO systems in the presence of channel aging effect 9 Chapter 3. Estimation of channel correlation matrix 13 3.1. Previous works 14 3.2. Proposed scheme 19 3.3. Performance evaluation 29 Chapter 4. Mobility-aware signal transmission in m-MIMO systems 43 4.1. Previous works 44 4.2. Proposed scheme 46 4.3. Performance evaluation 62 Chapter 5. Mobility-aware resource allocation in m-MIMO systems 73 5.1. Sum-rate-based greedy algorithm 74 5.2. Proposed scheme 76 5.3. Performance evaluation 88 Chapter 6. Conclusions 99 Appendix 103 References 105 Korean Abstract 115 Acknowledgement 119Docto

    The Road to Next-Generation Multiple Access: A 50-Year Tutorial Review

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    The evolution of wireless communications has been significantly influenced by remarkable advancements in multiple access (MA) technologies over the past five decades, shaping the landscape of modern connectivity. Within this context, a comprehensive tutorial review is presented, focusing on representative MA techniques developed over the past 50 years. The following areas are explored: i) The foundational principles and information-theoretic capacity limits of power-domain non-orthogonal multiple access (NOMA) are characterized, along with its extension to multiple-input multiple-output (MIMO)-NOMA. ii) Several MA transmission schemes exploiting the spatial domain are investigated, encompassing both conventional space-division multiple access (SDMA)/MIMO-NOMA systems and near-field MA systems utilizing spherical-wave propagation models. iii) The application of NOMA to integrated sensing and communications (ISAC) systems is studied. This includes an introduction to typical NOMA-based downlink/uplink ISAC frameworks, followed by an evaluation of their performance limits using a mutual information (MI)-based analytical framework. iv) Major issues and research opportunities associated with the integration of MA with other emerging technologies are identified to facilitate MA in next-generation networks, i.e., next-generation multiple access (NGMA). Throughout the paper, promising directions are highlighted to inspire future research endeavors in the realm of MA and NGMA.Comment: 43 pages, 38 figures; Submitted to Proceedings of the IEE

    MIMO designs for filter bank multicarrier and multiantenna systems based on OQAM

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    From the perspective of increasingly data rate requirements in mobile communications, it is deemed necessary to do further research so that the future goals can be reached. To that end, the radio-based communications are resorting to multicarrier modulations and spatial diversity. Until today, the orthogonal frequency division multiplexing (OFDM) modulation is regarded as the dominant technology. On one hand, the OFDM modulation is able to accommodate multiantenna configurations in a very straightforward manner. On the other hand, the poor stopband attenuation exhibited by the OFDM modulation, highlights that a definitely tight synchronization is required. In addition, the cyclic prefix (CP) has to be sufficiently long to avoid inter-block interference, which may substantially reduce the spectral efficiency. In order to overcome the OFDM drawbacks, the filter bank multicarrier modulation based on OQAM (FBMC/OQAM) is introduced. This modulation does not need any CP and benefits from pulse shaping techniques. This aspect becomes crucial in cognitive radio networks and communication systems where nodes are unlikely to be synchronized. In principle, the poor frequency confinement exhibited by OFDM should tip the balance towards FBMC/OQAM. However, the perfect reconstruction property of FBMC/OQAM systems does not hold in presence of multipath fading. This means that the FBMC/OQAM modulation is affected by inter-symbol and inter-carrier interference, unless the channel is equalized to some extent. This observation highlights that the FBMC/OQAM extension to MIMO architectures becomes a big challenge due to the need to cope with both modulation- and multiantenna-induced interference. The goal of this thesis is to study how the FBMC/OQAM modulation scheme can benefit from the degrees of freedom provided by the spatial dimension. In this regard, the first attempt to put the research on track is based on designing signal processing techniques at reception. In this case the emphasis is on single-input-multiple-output (SIMO) architectures. Next, the possibility of pre-equalizing the channel at transmission is investigated. It is considered that multiple antennas are placed at the transmit side giving rise to a multiple-input-single-output (MISO) configuration. In this scenario, the research is not only focused on counteracting the channel but also on distributing the power among subcarriers. Finally, the joint transmitter and receiver design in multiple-input-multiple-output (MIMO) communication systems is covered. From the theory developed in this thesis, it is possible to conclude that the techniques originally devised in the OFDM context can be easily adapted to FBMC/OQAM systems if the channel frequency response is flat within the subchannels. However, metrics such as the peak to average power ratio or the sensitivity to the carrier frequency offset constraint the number of subcarriers, so that the frequency selectivity may be appreciable at the subcarrier level. Then, the flat fading assumption is not satisfied and the specificities of FBMC/OQAM systems have to be considered. In this situation, the proposed techniques allow FBMC/OQAM to remain competitive with OFDM. In addition, for some multiantenna configurations and propagation conditions FBMC/OQAM turns out to be the best choice. The simulation-based results together with the theoretical analysis conducted in this thesis contribute to make progress towards the application of FBMC/OQAM to MIMO channels. The signal processing techniques that are described in this dissertation allow designers to exploit the potentials of FBMC/OQAM and MIMO to improve the link reliability as well as the spectral efficiency

    Interference Mitigation through Successive Cancellation in Heterogeneous Networks

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    Downlink Achievable Rate Analysis for FDD Massive MIMO Systems

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    Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, are a very promising direction for 5G due to their ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this dissertation, we improve the performance of FDD massive MIMO networks in terms of downlink training overhead reduction, by designing an efficient downlink beamforming method and developing a new algorithm to estimate the channel state information based on compressive sensing techniques. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink direction of arrivals (DoAs) and downlink direction of departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that selects the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional methods in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming methods

    Advanced Signal Processing Techniques for Two-Way Relaying Networks and Full-Duplex Communication Systems

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    ๏ปฟSehr hohe Datenraten und stรคndig verfรผgbare Netzabdeckung in zukรผnftigen drahtlosen Netzwerken erfordern neue Algorithmen auf der physischen Schicht. Die Nutzung von Relais stellt ein vielversprechendes Verfahren dar, da die Netzabdeckung gesteigert werden kann. Zusรคtzlich steht hierdurch im Vergleich zu Kupfer- oder Glasfaserleitungen eine preiswerte Lรถsung zur Anbindung an die Netzinfrastruktur zur Verfรผgung. Traditionelle Einwege-Relais-Techniken (One-Way Relaying [OWR]) nutzen Halbduplex-Verfahren (HD-Verfahren), welche das รœbertragungssystem ausbremst und zu spektralen Verlusten fรผhrt. Einerseits erlauben es Zweiwege-Relais-Techniken (Two-Way Relaying [TWR]), simultan sowohl an das Relais zu senden als auch von diesem zu empfangen, wodurch im Vergleich zu OWR das Spektrum effizienter genutzt wird. Aus diesem Grunde untersuchen wir Zweiwege-Relais und im Speziellen TWR-Systeme fรผr den Mehrpaar-/Mehrnutzer-Betrieb unter Nutzung von Amplify-and-forward-Relais (AF-Relais). Derartige Szenarien leiden unter Interferenzen zwischen Paaren bzw. zwischen Nutzern. Um diesen Interferenzen Herr zu werden, werden hochentwickelte Signalverarbeitungsalgorithmen โ€“ oder in anderen Worten rรคumliche Mehrfachzugriffsverfahren (Spatial Division Multiple Access [SDMA]) โ€“ benรถtigt. Andererseits kann der spektrale Verlust durch den HD-Betrieb auch kompensiert werden, wenn das Relais im Vollduplexbetrieb arbeitet. Nichtsdestotrotz ist ein FD-Gerรคt in der Praxis aufgrund starker interner Selbstinterferenz (SI) und begrenztem Dynamikumfang des Tranceivers schwer zu realisieren. Aus diesem Grunde sollten fortschrittliche Verfahren zur SI-รœnterdrรผckung entwickelt werden. Diese Dissertation trรคgt diesen beiden Zielen Rechnung, indem optimale und/oder effiziente algebraische Lรถsungen entwickelt werden, welche verschiedenen Nutzenfunktionen, wie Summenrate und minimale Sendeleistung, maximieren.Im ersten Teil studieren wir zunรคchst Mehrpaar-TWR-Netzwerke mit einem einzelnen Mehrantennen-AF-Relais. Dieser Anwendungsfall kann auch so betrachtet werden, dass sich mehrere verschiedene Dienstoperatoren Relais und Spektrum teilen, wobei verschiedene Nutzerpaare zu verschiedenen Dienstoperatoren gehรถren. Aktuelle Ansรคtzen zielen auf Interferenzunterdrรผckung ab. Wir schlagen ein auf Projektion basiertes Verfahren zur Trennung mehrerer Dienstoperatoren (projection based separation of multiple operators [ProBaSeMO]) vor. ProBaSeMO ist leicht anpassbar fรผr den Fall, dass jeder Nutzer mehrere Antennen besitzt oder unterschiedliche Systemdesignkriterien angewendet werden mรผssen. Als BewertungsmaรŸstab fรผr ProBaSeMO entwickeln wir optimale Algorithmen zur Maximierung der Summenrate, zur Minimierung der Sendeleistung am Relais oder zur Maximierung des minimalen Signal-zu-Interferenz-und-Rausch-Verhรคltnisses (Signal to Interference and Noise Ratio [SINR]) am Nutzer. Zur Maximierung der Summenrate wurden spezifische gradientenbasierte Methoden entwickelt, die unabhรคngig davon sind, ob ein Nutzer mit einer oder mehr Antennen ausgestattet ist. Um im Falle eines โ€žWorst-Caseโ€œ immer noch eine polynomielle Laufzeit zu garantieren, entwickelten wir einen Algorithmus mit polynomieller Laufzeit. Dieser ist inspiriert von der โ€žPolynomial Time Difference of Convex Functionsโ€œ-Methode (POTDC-Methode). Bezรผglich der Summenrate des Systems untersuchen wir zuletzt, welche Bedingungen erfรผllt sein mรผssen, um einen Gewinn durch gemeinsames Nutzen zu erhalten. Hiernach untersuchen wir die Maximierung der Summenrate eines Mehrpaar-TWR-Netzwerkes mit mehreren Einantennen-AF-Relais und Einantennen-Nutzern. Das daraus resultierende Problem der Summenraten-Maximierung, gebunden an eine bestimmte Gesamtsendeleistung aller Relais im Netzwerk, ist รคhnlich dem des vorangegangenen Szenarios. Dementsprechend kann eine optimale Lรถsung fรผr das eine Szenario auch fรผr das jeweils andere Szenario genutzt werden. Weiterhin werden basierend auf dem Polynomialzeitalgorithmus global optimale Lรถsungen entwickelt. Diese Lรถsungen sind entweder an eine maximale Gesamtsendeleistung aller Relais oder an eine maximale Sendeleistung jedes einzelnen Relais gebunden. Zusรคtzlich entwickeln wir suboptimale Lรถsungen, die effizient in ihrer Laufzeit sind und eine Approximation der optimalen Lรถsung darstellen. Hiernach verlegen wir unser Augenmerk auf ein Mehrpaar-TWR-Netzwerk mit mehreren Mehrantennen-AF-Relais und mehreren Repeatern. Solch ein Szenario ist allgemeiner, da die vorherigen beiden Szenarien als spezielle Realisierungen dieses Szenarios aufgefasst werden kรถnnen. Das Interferenz-Management in diesem Szenario ist herausfordernder aufgrund der vorhandenen Repeater. Interferenzneutralisierung (IN) stellt eine Lรถsung dar, um diese Art Interferenz zu handhaben. Im Zuge dessen werden notwendige und ausreichende Bedingungen zur Aufhebung der Interferenz hergeleitet. Weiterhin wird ein Framework entwickelt, dass verschiedene Systemnutzenfunktionen optimiert, wobei IN im jeweiligen Netzwerk vorhanden sein kann oder auch nicht. Dies ist unabhรคngig davon, ob die Relais einer maximalen Gesamtsendeleistung oder einer individuellen maximalen Sendeleistung unterliegen. Letztendlich entwickeln wir ein รœbertragungsverfahren sowie ein Vorkodier- und Dekodierverfahren fรผr Basisstationen (BS) in einem TWR-assistierten Mehrbenutzer-MIMO-Downlink-Kanal. Im Vergleich mit dem Mehrpaar-TWR-Netzwerk leidet dieses Szenario unter Interferenzen zwischen den Kanรคlen. Wir entwickeln drei suboptimale Algorithmen, welche auf Kanalinversion basieren. ProBaSeMO und โ€žZero-Forcing Dirty Paper Codingโ€œ (ZFDPC), welche eine geringe Zeitkomplexitรคt aufweisen, schaffen eine Balance zwischen Leistungsfรคhigkeit und Komplexitรคt. Zusรคtzlich gibt es jeweils nur geringe Einbrรผche in stark beanspruchten Kommunikationssystemen.Im zweiten Teil untersuchen wir Techniken zur SI-Unterdrรผckung, um den FD-Gewinn in einem Punkt-zu-Punkt-System auszunutzen. Zunรคchst entwickeln wir ein รœbertragungsverfahren, dass auf SI Rรผcksicht nimmt und die SI-Unterdrรผckung gegen den Multiplexgewinn abwรคgt. Die besten Ergebnisse werden durch die perfekte Kenntnis des Kanals erzielt, was praktisch nicht genau der Fall ist. Aus diesem Grund werden รœbertragungstechniken fรผr den โ€žWorst Caseโ€œ entwickelt, die den Kanalschรคtzfehlern Rechnung tragen. Diese Fehler werden deterministisch modelliert und durch Ellipsoide beschrรคnkt. In praktischen Szenarien ist der HF-Schaltkreise nicht perfekt. Dies hat Einfluss auf die Verfahren zur SI-Unterdrรผckung und fรผhrt zu einer Restselbstinterferenz. Wir entwickeln effiziente รœbertragungstechniken mittels Beamforming, welche auf dem Signal-zu-Verlust-und-Rausch-Verhรคltnis (signal to leakage plus noise ratio [SLNR]) aufbauen, um Unvollkommenheiten der HF-Schaltkreise auszugleichen. Zusรคtzlich kรถnnen alle Designkonzepte auf FD-OWR-Systeme erweitert werden.To enable ultra-high data rate and ubiquitous coverage in future wireless networks, new physical layer techniques are desired. Relaying is a promising technique for future wireless networks since it can boost the coverage and can provide low cost wireless backhauling solutions, as compared to traditional wired backhauling solutions via fiber and copper. Traditional one-way relaying (OWR) techniques suffer from the spectral loss due to the half-duplex (HD) operation at the relay. On one hand, two-way relaying (TWR) allows the communication partners to transmit to and/or receive from the relay simultaneously and thus uses the spectrum more efficiently than OWR. Therefore, we study two-way relays and more specifically multi-pair/multi-user TWR systems with amplify-and-forward (AF) relays. These scenarios suffer from inter-pair or inter-user interference. To deal with the interference, advanced signal processing algorithms, in other words, spatial division multiple access (SDMA) techniques, are desired. On the other hand, if the relay is a full-duplex (FD) relay, the spectral loss due to a HD operation can also be compensated. However, in practice, a FD device is hard to realize due to the strong loop-back self-interference and the limited dynamic range at the transceiver. Thus, advanced self-interference suppression techniques should be developed. This thesis contributes to the two goals by developing optimal and/or efficient algebraic solutions for different scenarios subject to different utility functions of the system, e.g., sum rate maximization and transmit power minimization. In the first part of this thesis, we first study a multi-pair TWR network with a multi-antenna AF relay. This scenario can be also treated as the sharing of the relay and the spectrum among multiple operators assuming that different pairs of users belong to different operators. Existing approaches focus on interference suppression. We propose a projection based separation of multiple operators (ProBaSeMO) scheme, which can be easily extended when each user has multiple antennas or when different system design criteria are applied. To benchmark the ProBaSeMO scheme, we develop optimal relay transmit strategies to maximize the system sum rate, minimize the required transmit power at the relay, or maximize the minimum signal to interference plus noise ratio (SINR) of the users. Specifically for the sum rate maximization problem, gradient based methods are developed regardless whether each user has a single antenna or multiple antennas. To guarantee a worst-case polynomial time solution, we also develop a polynomial time algorithm which has been inspired by the polynomial time difference of convex functions (POTDC) method. Finally, we analyze the conditions for obtaining the sharing gain in terms of the sum rate. Then we study the sum rate maximization problem of a multi-pair TWR network with multiple single antenna AF relays and single antenna users. The resulting sum rate maximization problem, subject to a total transmit power constraint of the relays in the network, yields a similar problem structure as in the previous scenario. Therefore the optimal solution for one scenario can be used for the other. Moreover, a global optimal solution, which is based on the polyblock approach, and several suboptimal solutions, which are more computationally efficient and approximate the optimal solution, are developed when there is a total transmit power constraint of the relays in the network or each relay has its own transmit power constraint. We then shift our focus to a multi-pair TWR network with multiple multi-antenna AF relays and multiple dumb repeaters. This scenario is more general because the previous two scenarios can be seen as special realizations of this scenario. The interference management in this scenario is more challenging due to the existence of the repeaters. Interference neutralization (IN) is a solution for dealing with this kind of interference. Thereby, necessary and sufficient conditions for neutralizing the interference are derived. Moreover, a general framework to optimize different system utility functions in this network with or without IN is developed regardless whether the AF relays in the network have a total transmit power limit or individual transmit power limits. Finally, we develop the relay transmit strategy as well as base station (BS) precoding and decoding schemes for a TWR assisted multi-user MIMO (MU-MIMO) downlink channel. Compared to the multi-pair TWR network, this scenario suffers from the co-channel interference. We develop three suboptimal algorithms which are based on channel inversion, ProBaSeMO and zero-forcing dirty paper coding (ZFDPC), which has a low computational complexity, provides a balance between the performance and the complexity, and suffers only a little when the system is heavily loaded, respectively.In the second part of this thesis, we investigate self-interference (SI) suppression techniques to exploit the FD gain for a point-to-point MIMO system. We first develop SI aware transmit strategies, which provide a balance between the SI suppression and the multiplexing gain of the system. To get the best performance, perfect channel state information (CSI) is needed, which is imperfect in practice. Thus, worst case transmit strategies to combat the imperfect CSI are developed, where the CSI errors are modeled deterministically and bounded by ellipsoids. In real word applications, the RF chain is imperfect. This affects the performance of the SI suppression techniques and thus results in residual SI. We develop efficient transmit beamforming techniques, which are based on the signal to leakage plus noise ratio (SLNR) criterion, to deal with the imperfections in the RF chain. All the proposed design concepts can be extended to FD OWR systems

    Resource Allocation in Multi-user MIMO Networks: Interference Management and Cooperative Communications

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    Nowadays, wireless communications are becoming so tightly integrated in our daily lives, especially with the global spread of laptops, tablets and smartphones. This has paved the way to dramatically increasing wireless network dimensions in terms of subscribers and amount of flowing data. Therefore, the two important fundamental requirements for the future 5G wireless networks are abilities to support high data traffic and exceedingly low latency. A likely candidate to fulfill these requirements is multicell multi-user multi-input multiple-output (MU-MIMO); also termed as coordinated multi-point (CoMP) transmission and reception. To achieve the highest possible performance in MU-MIMO networks, a properly designed resource allocation algorithm is needed. Moreover, with the rapidly growing data traffic, interference has become a major limitation in wireless networks. Interference alignment (IA) has been shown to significantly manage the interference and improve the network performance. However, how practically use IA to mitigate interference in a downlink MU-MIMO network still remains an open problem. In this dissertation, we improve the performance of MU-MIMO networks in terms of spectral efficiency, by designing and developing new beamforming algorithms that can efficiently mitigate the interference and allocate the resources. Then we mathematically analyze the performance improvement of MUMIMO networks employing proposed techniques. Fundamental relationships between network parameters and the network performance is revealed, which provide guidance on the wireless networks design. Finally, system level simulations are conducted to investigate the performance of the proposed strategies
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