9 research outputs found

    Graph Neural Networks-Based User Pairing in Wireless Communication Systems

    Full text link
    Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image processing tasks, are not optimized for wireless network problems. As network size increases, these methods get harder to train and generalize. User pairing is one such essential NP-hard optimization problem in wireless communication systems that entails selecting users to be scheduled together while minimizing interference and maximizing throughput. In this paper, we propose an unsupervised graph neural network (GNN) approach to efficiently solve the user pairing problem. Our proposed method utilizes the Erdos goes neural pipeline to significantly outperform other scheduling methods such as k-means and semi-orthogonal user scheduling (SUS). At 20 dB SNR, our proposed approach achieves a 49% better sum rate than k-means and a staggering 95% better sum rate than SUS while consuming minimal time and resources. The scalability of the proposed method is also explored as our model can handle dynamic changes in network size without experiencing a substantial decrease in performance. Moreover, our model can accomplish this without being explicitly trained for larger or smaller networks facilitating a dynamic functionality that cannot be achieved using CNNs or MLPs

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

    Get PDF
    ํ•™์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When we transmit signal to users in high mobility, we may experience serious performance loss due to the inaccuracy of outdated CSI, associated with so-called channel aging effect.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

    Topology Control, Scheduling, and Spectrum Sensing in 5G Networks

    Get PDF
    The proliferation of intelligent wireless devices is remarkable. To address phenomenal traffic growth, a key objective of next-generation wireless networks such as 5G is to provide significantly larger bandwidth. To this end, the millimeter wave (mmWave) band (20 GHz -300 GHz) has been identified as a promising candidate for 5G and WiFi networks to support user data rates of multi-gigabits per second. However, path loss at mmWave is significantly higher than today\u27s cellular bands. Fortunately, this higher path loss can be compensated through the antenna beamforming technique-a transmitter focuses a signal towards a specific direction to achieve high signal gain at the receiver. In the beamforming mmWave network, two fundamental challenges are network topology control and user association and scheduling. This dissertation proposes solutions to address these two challenges. We also study a spectrum sensing scheme which is important for spectrum sharing in next-generation wireless networks. Due to beamforming, the network topology control in mmWave networks, i.e., how to determine the number of beams for each base station and the beam coverage, is a great challenge. We present a novel framework to solve this problem, termed Beamforming Oriented tOpology coNtrol (BOON). The objective is to reduce total downlink transmit power of base stations in order to provide coverage of all users with a minimum quality of service. BOON smartly groups nearby user equipment into clusters to dramatically reduce interference between beams and base stations so that we can significantly reduce transmit power from the base station. We have found that on average BOON uses only 10%, 32%, and 25% transmit power of three state-of-the-art schemes in the literature. Another fundamental problem in the mmWave network is the user association and traffic scheduling, i.e., associating users to base stations, and scheduling transmission of user traffic over time slots. This is because base station has a limited power budget and users have very diverse traffic, and also require some minimum quality of service. User association is challenging because it generally does not rely on the user distance to surrounding base stations but depends on if a user is covered by a beam. We develop a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink user assOciation Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time of all users\u27 traffic. On average, BOOST reduces the transmission time by 37%, 30%, and 26% compared with the three state-of-the-art user scheduling schemes in the literature. At last, we present a wavelet transform based spectrum sensing scheme that can simultaneously sense multiple subbands, even without knowing how the subbands are divided, i.e., their boundaries. It can adaptively detect all active subband signals and, thus, discover the residual spectrum that can be used by unlicensed devices

    Block diagonalization techniques for cellular networks: clustering and scheduling

    Get PDF
    Menciรณn Internacional en el tรญtulo de doctorLa necesidad de tasas de transmisiรณn mรกs elevadas y una mayor eficiencia en las redes celulares es la principal motivaciรณn para considerar el uso de UFR. La coordinaciรณn entre BSs se hace necesaria, entonces, para compensar los problemas introducidos por la interferencia. La coordinaciรณn global de la red es demasiado compleja y, ademรกs, presenta limitaciones intrรญnsecas, que impiden su utilizaciรณn en escenarios reales. La utilizaciรณn de grupos reducidos de BSs es una alternativa para reducir los requisitos impuestos por la coordinaciรณn. Como consecuencia de la agrupaciรณn, aparece OCI, la cual perjudica seriamente las comunicaciones. Este trabajo se centra en BD, una tรฉcnica de precodificaciรณn lineal que combina unas buenas prestaciones con una complejidad relativamente baja. Sin embargo, la interferencia empeora notablemente su funcionamiento. En esta tesis se estudia el canal descendente de una red celular conglomerada, donde se usa BD para coordinar las BSs que forman cada grupo. Se analiza la tasa media obtenible como funciรณn de mรบltiples parรกmetros del escenario. De especial interรฉs es la dependencia con el tamaรฑo de las agrupaciones, de donde se desprende que existe un tamaรฑo รณptimo para los grupos de BSs, por encima del cual no se obtienen mejoras significativas. La equidad del sistema se estudia en presencia de OCI, tambiรฉn como funciรณn de diversos parรกmetros del escenario, como puede ser la asignaciรณn de potencia. Se propone una estrategia mixta de transmisiรณn, que combina BD con procesado SU, como mecanismo para combatir las dificultades introducidas por la interferencia que no se gestiona. La estrategia consiste en dos fases: โ€ข Los usuarios deciden localmente quรฉ estrategia prefieren para la transmisiรณn, y envรญan esta informaciรณn a las BSs. โ€ข Las BSs utilizan las decisiones recibidas para planificar las transmisiones, de modo que se pueda optimizar el funcionamiento de la red. El resultado de la estrategia propuesta es una mejora de las prestaciones de BD en presencia de OCI, especialmente para los usuarios mรกs desfavorecidos. Esto se traduce en que, adicionalmente, el sistema se vuelve mรกs justo, al mismo tiempo que el rendimiento de la red aumenta.The need for higher data rates and higher efficiency in cellular networks motivates the use of Universal Frequency Reuse (UFR). Coordination among Base Stations (BSs) is required then to alleviate the performance penalty due to the interference. Global coordination is too complex and has inherent limitations that prevents it from being used in real world scenarios. Clusters of a reduced number of BSs can be considered in order to ease off the requirements of coordination. As a result, Other Cluster Interference (OCI) appears, affecting negatively the communications. This work focuses on Block Diagonalization (BD), a linear precoding technique that combines a good theoretical performance with a relatively low complexity. However, the unwanted interference seriously impacts the results obtained using BD. This thesis studies the downlink of a clustered cellular network, where BD is used to coordinate the BS within each cluster. The mean achievable rate is analyzed as a function of several scenario parameters. Of particular interest is the dependence on the cluster size, which yields that there is an optimum cluster size, beyond which no significant gain is obtained. Fairness considerations are analyzed in the presence of OCI, also studied as a function of scenario parameters such as the power allocation. A mixed strategy using BD and Single User (SU) processing is proposed as a means to overcome the impairment of the unhandled interference. The transmission consists of two stages: โ€ข Users locally decide which transmission strategy they prefer and send this information to the BSs. โ€ข BSs use the decisions of all users to schedule them for transmission so that the performance of the network is optimized. The result of the proposed strategy is an improvement in the performance of BD in the presence of OCI, especially for the users experiencing the worst conditions. This means that the fairness of the system is also increased, along with the overall performance of the network.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Lajos Hanzo.- Secretario: Raquel Perez Leal.- Vocal: Atilio Manuel da Silva Gameir

    Multiuser Scheduling and Hierarchical Codebook Design Techniques for MIMO Systems

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 8. ์ด์ •์šฐ.๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์€ ์‹œ์Šคํ…œ ์„ฑ๋Šฅ ์ธก๋ฉด์—์„œ ๋‹จ์ผ์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ๋ณด๋‹ค ์ด์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฐ ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ๋Š” ๊ณ ๋ คํ•ด์•ผ ๋  ๋ช‡๊ฐ€์ง€ ์‚ฌํ•ญ๋“ค์ด ์žˆ๋‹ค. ์šฐ์„ , ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ๋Š” ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ฐ„์„ญ์ด ๋ฐœ์ƒํ•˜๊ฒŒ ๋˜๊ณ  ๊ทธ๊ฒƒ์€ ์‹œ์Šคํ…œ ์„ฑ๋Šฅ์— ์ œ์•ฝ์„ ์ค€๋‹ค. Zero-Forcing beamforming (ZFBF)๊ณผ Block Diagonalization (BD)์ด ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ฐ„์„ญ์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์–ด์ง€๋Š” ์„ ํ˜• ํ”„๋ฆฌ์ฝ”๋”ฉ ๊ธฐ๋ฒ•์ด๋‹ค. ๋‘๋ฒˆ์งธ๋กœ ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์…€๋ฃฐ๋Ÿฌ ํ™˜๊ฒฝ์—์„œ ์ „์ฒด์ ์ธ ์„ฑ๋Šฅ์„ ์ตœ๋Œ€ํ™” ์‹œํ‚ค๋Š” ์‚ฌ์šฉ์ž ๊ทธ๋ฃน์„ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ๋„ ์•„์ฃผ ์ค‘์š”ํ•œ ๋ฌธ์ œ ์ค‘์˜ ํ•˜๋‚˜์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ์ ์˜ ์Šค์ผ€์ฅด๋ง ๊ธฐ๋ฒ•์€ ์…€ ๋‚ด์˜ ์‚ฌ์šฉ์ž์˜ ์ˆซ์ž๊ฐ€ ํด ๋•Œ ๊ณ„์‚ฐ๋Ÿ‰์ด ๋„ˆ๋ฌด ๋ณต์žกํ•ด์„œ ์‚ฌ์šฉ๋  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๊ฐ€์ง€๋Š” ์Šค์ผ€์ฅด๋ง ๊ธฐ๋ฒ•์ด ๋ฐ˜๋“œ์‹œ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์ค‘์š”ํ•œ ์‚ฌํ•ญ ์ค‘์˜ ํ•˜๋‚˜๋Š” ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ์˜ ์ฝ”๋“œ๋ถ ์„ค๊ณ„ ๊ธฐ๋ฒ•์ด๋‹ค. ์‹ค์งˆ์ ์ธ ์‹œ์Šคํ…œ์—์„œ ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์ด ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ฐ„์„ญ์— ๋งค์šฐ ๋ฏผ๊ฐํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐ์ง€๊ตญ์ด ๊ฐ ์‚ฌ์šฉ์ž์˜ ์ฑ„๋„์ •๋ณด๋ฅผ ์•„๋Š” ๊ฒƒ์ด ์„ฑ๋Šฅ ํ–ฅ์ƒ์— ๋„์›€์„ ์ค€๋‹ค. ์ฑ„๋„์ •๋ณด์˜ ํš๋“์„ ์œ„ํ•ด ๊ฐ€์žฅ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ๋ฐฉ๋ฒ•์ด ์ฑ„๋„์„ ์–‘์žํ™”ํ•˜๋Š” ์ฝ”๋“œ๋ถ ๊ธฐ๋ฒ•์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฝ”๋“œ๋ถ ๊ธฐ๋ฒ•์€ ๊ทธ๊ฒƒ์˜ ์‚ฌ์ด์ฆˆ๊ฐ€ ์ปค์งˆ ๋•Œ ์ฝ”๋“œ๋ถ ๋‚ด์˜ ์ตœ์ ์˜ ์ฝ”๋“œ์›Œ๋“œ๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•œ ๊ณ„์‚ฐ ๋ณต์žก๋„๊ฐ€ ์ง€์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ณต์žก๋„์˜ ๋ฌธ์ œ๋ฅผ ๊ฐ€์ง€๊ฒŒ ๋œ๋‹ค. ์ด๋ฒˆ ์กธ์—…๋…ผ๋ฌธ์—์„œ ์šฐ๋ฆฌ๋Š” 5๊ฐœ์˜ ์ฑ•ํ„ฐ๋ฅผ ํ†ตํ•ด ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์Šค์ผ€์ฅด๋ง ๋ฐ ์ฝ”๋“œ๋ถ ์„ค๊ณ„ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ์งธ๋กœ, ์†ก์‹ ๋‹จ์ด ์ˆ˜์‹ ๋‹จ๋“ค์˜ ์ฑ„๋„์„ ์™„๋ฒฝํžˆ ์•ˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•  ๋•Œ ์ฝ”๋‹ฌ๊ฑฐ๋ฆฌ์™€ BD๋ฅผ ์ด์šฉํ•œ ๋‚ฎ์€ ๋ณต์žก๋„์˜ ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” ์—ฌ๋Ÿฌ ์‚ฌ์šฉ์ž๋“ค ์‚ฌ์ด์˜ ์ง๊ต์„ฑ์˜ ์ธก์ •๋„๋กœ์จ ์ฝ”๋‹ฌ๊ฑฐ๋ฆฌ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ง๊ต์„ฑ์€ BD์— ์˜ํ•œ ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹ค์ค‘์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ๊ณ ๋ ค์‚ฌํ•ญ์ด๋‹ค. ๋‘˜์งธ๋กœ, ์šฐ๋ฆฌ๋Š” ์ตœ์ ์˜ ์Šค์ผ€์ฅด๋ง ๊ธฐ๋ฒ•๊ณผ ๋น„๊ตํ•˜์—ฌ ์„ฑ๋Šฅ ์—ดํ™”๋ฅผ ์ตœ์†Œํ™”ํ•จ๊ณผ ๋™์‹œ์— ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๊ฐ€์ง€๋Š” determinant ๊ธฐ๋ฐ˜์˜ ์Šค์ผ€์ฅด๋ง ๊ธฐ๋ฒ•๊ณผ ์ฑ„๋„ ์ธก์ •์„ ์œ„ํ•œ ํŒŒ์ผ๋Ÿฟ์„ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ƒˆ๋กœ ์ œ์•ˆ๋œ ํŒŒ์ผ๋Ÿฟ ๊ธฐ๋ฒ•์ด determinant ๊ธฐ๋ฐ˜์˜ ์Šค์ผ€์ฅด๋ง ๊ธฐ๋ฒ•๊ณผ ๊ฒฐํ•ฉ๋œ๋‹ค. ์„ธ๋ฒˆ์งธ๋กœ, ์šฐ๋ฆฌ๋Š” ์„ฑ๋Šฅ ์ธก์ •๋„๋กœ์„œ ์ฑ„๋„ ์šฉ๋Ÿ‰์ด ์•„๋‹Œ ๋น„ํŠธ ์˜ค๋ฅ˜์œจ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ƒˆ๋กœ์šด ์Šค์ผ€์ฅด๋ง ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ ๋‘ ๊ฐ€์ง€ ์กฐ๊ฑด์—์„œ์˜ ํŒŒ์›Œํ• ๋‹น ๊ธฐ๋ฒ•๋„ ๊ฐ™์ด ์ œ์•ˆํ•œ๋‹ค. ์ฒซ๋ฒˆ์งธ ์กฐ๊ฑด์€ ์ฃผ์–ด์ง„ ๋ฐ์ดํ„ฐ์œจ์—์„œ ๋น„ํŠธ์˜ค๋ฅ˜์œจ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ๊ฒƒ์ด๊ณ  ๋‘๋ฒˆ์งธ ์กฐ๊ฑด์€ ๋ชฉํ‘œ ๋น„ํŠธ์˜ค๋ฅ˜์œจ ๋‚ด์—์„œ ๋ฐ์ดํ„ฐ์œจ์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ œ์•ˆํ•˜๋Š” ์Šค์ผ€์ฅด๋ง ๊ธฐ๋ฒ•์€ ๋‘ ๊ฐ€์ง€ ์กฐ๊ฑด์„ ๋ชจ๋‘ ๊ณ ๋ คํ•ด์„œ ์„ค๊ณ„๋˜๊ณ  ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๊ฐ€์ง„๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•๋“ค์˜ ํ•ต์‹ฌ ์ค‘์˜ ํ•˜๋‚˜๋Š” ์Šค์ผ€์ฅด๋ง์„ ๊ณ ๋ คํ•  ๋•Œ ์ฑ„๋„ ์šฉ๋Ÿ‰์ด ์•„๋‹Œ ๋น„ํŠธ์˜ค๋ฅ˜์œจ์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด๊ณ  ๋‹ค๋ฅธ ํ•ต์‹ฌ ํฌ์ธํŠธ๋Š” ๋น„ํŠธ์˜ค๋ฅ˜์œจ์„ ์ตœ์†Œํ™”์‹œํ‚ค๊ฑฐ๋‚˜ ๋ฐ์ดํ„ฐ์œจ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํŒŒ์›Œ ํ• ๋‹น ๊ธฐ๋ฒ• ์ œ์•ˆ์ด๋‹ค. ๋„ท์งธ๋กœ, ์šฐ๋ฆฌ๋Š” ์ตœ์†Œํ•œ์˜ ์„ฑ๋Šฅ ์—ดํ™”์™€ ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๊ณ„์ธต์  ๊ตฌ์กฐ๋ฅผ ์ง€๋‹Œ ์„ธ๊ฐ€์ง€ ์ฝ”๋“œ๋ถ ์„ค๊ณ„ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. i.i.d. ์ฑ„๋„์—์„œ ํ•˜๋‚˜์˜ ๋ถ€๋ชจ ์ฝ”๋“œ๋ถ์„ ๊ฐ€์ง€๊ณ  ์Šค์Šค๋กœ ์ž์‹ ์ฝ”๋“œ๋ถ์„ ์ƒ์„ฑํ•˜๋Š” ๊ธฐ๋ฒ•๊ณผ ๋‘ ๊ฐœ์˜ ๋ถ€๋ชจ ์ฝ”๋“œ๋ถ์„ ์—ฐ๊ฒฐ์‹œํ‚ค๋Š” ์ฝ”๋“œ๋ถ ์—ฐ๊ฒฐ ๊ธฐ๋ฒ•์ด ์ œ์•ˆ๋œ๋‹ค. ๋˜ํ•œ ์‹œ๊ฐ„ ์—ฐ๊ด€์„ฑ์ด ์žˆ๋Š” ์ฑ„๋„์—์„œ ์ฝ”๋“œ๋ถ ํฌ๊ธฐ๋ฅผ ์ค„์ด๋Š” ๊ณ„์ธต์  ๊ตฌ์กฐ์˜ ์ฝ”๋“œ๋ถ ์„ค๊ณ„ ๊ธฐ๋ฒ•๋„ ์ œ์•ˆ๋œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•๋“ค์˜ ํ•ต์‹ฌ์€ ์ž์‹ ์ฝ”๋“œ๋ถ์ด ์ฝ”๋‹ฌ๊ฑฐ๋ฆฌ์— ๊ธฐ๋ฐ˜์„ ๋‘” centroid ๊ธฐ๋ฒ•์— ์˜ํ•ด ์„ค๊ณ„๋˜๋Š” ๊ฒƒ์ด๋‹ค. ๋‹ค์„ฏ์งธ๋กœ, ์šฐ๋ฆฌ๋Š” ZFBF๊ณผ PU2RC์˜ ์ด์ ์„ ๋™์‹œ์— ๊ฐ€์ง€๋Š” ์ƒˆ๋กœ์šด ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹จ์ผ์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๊ณ  ๊ทธ๊ฒƒ์˜ ์–‘์žํ™” ์—๋Ÿฌ์™€ ๋ฐ์ดํ„ฐ์œจ์„ ๋ถ„์„ํ•œ๋‹ค. ๋ถ„์„์€ ํ”ผ๋“œ๋ฐฑ ๋น„ํŠธ์ˆ˜์™€ ์‚ฌ์šฉ์ž์˜ ์ˆ˜, ์‹ ํ˜ธ ํŒŒ์›Œ๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ฐ์ดํ„ฐ์œจ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ๋ณด์—ฌ์ค€๋‹ค. ์“ฐ๋ฃจํ’‹ ์Šค์ผ€์ผ๋ง ๋ฒ•์น™์ด ์ค‘๊ฐ„๊ณผ ๋†’์€ SNR ์˜์—ญ์—์„œ ์œ ๋„๋œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋˜ํ•œ ์ œ์•ˆ๋œ ์ƒˆ๋กœ์šด ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ๋‹ค์ค‘์‚ฌ์šฉ์ž ๋‹จ์ผ์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ์—์„œ์˜ ์ƒˆ๋กœ์šด ์ฝ”๋“œ๋ถ ์„ค๊ณ„ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ทธ๊ฒƒ์€ ๊ณ„์ธต์  ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๊ณ  ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค.Multiuser MIMO (MU-MIMO) systems have advantages over single-user MIMO systems in terms of system performance. There are some issues to consider for the MU-MIMO systems. In MU-MIMO systems, at first, inter-user interference is unavoidable, and it limits the system performance. A Zero-Forcing Bemaforming (ZFBF) and a Block Diagonalization (BD) methods are linear precoding techniques that are widely used to eliminate the inter-user interference. Second, it is one of critical issues to select a user group which maximizes the overall throughput of the system in a MU-MIMO cellular system, where there are many candidate users. However, the optimal scheduling strategy (exhaustive user selection) is computationally prohibitive when the total number of users is large and thus low complexity MU-MIMO scheduling schemes should be considered. Another one of the important is a codebook design issue for MU-MIMO systems. In practical systems, it is better for the transmitter to know channel state informations of each receiver especially in MU-MIMO systems since the MU-MIMO systems are very sensitive to inter-user interference. The most widely used among schemes for channel knowledge in the transmitter are codebook techniques, which quantize channels with fixed size. However the codebook schemes have a complexity problem when codebook size is large because compuational complexity for finding the best codeword in a codebook increases exponentially with codebook size. In this dissertation, we propose schedulings and codebook designs for MU-MIMO systems throughout 5 chapters. First, we propose a low complexity MU-MIMO scheduling scheme using BD with chordal distance assuming perfect channel knowledge at the transmitter. One of the key idea of this scheme is to use chordal distance as a measure of orthogonality between different users since orthogonality is very critical issue in MU-MIMO scheduling by BD. Second, we propose a determinant based user selection algorithm which reduces the search complexity without much performance degradation and a new pilot scheme with only one set of pilot. The new pilot scheme is combined with the proposed scheduling algorithm. Third, we propose new MIMO scheduling techniques based on BER instead of capacity as the performance measure. We also propose two different scheduling strategies with power allocation. One is to minimize BER with a given rate, and the other is to maximize throughput (sum-rate) with a target BER constraint. We also propose a low complexity BER based MIMO scheduling algorithm with the two different strategies, which has lower complexity than the conventional capacity based algorithm. One of the key contributions of the proposed schemes is to use BER instead of capacity as the user selection metric, and another is the novel power allocation techniques for the BER minimization and the throughput maximization strategies. Fourth, we propose three codebook design methods with hierarchical structure to reduce the complexity with minimal performance loss. For an i.i.d. channel, a self-regenerative method which starts with one parent codebook and a codebook mapping method which starts with two parent codebooks are proposed. For a time-correlated channel, we propose a differential feedback method using only the 2nd stage codebook for channel feed back. A key contribution of the proposed schemes is that the 2nd stage codebook is designed with the centroid based on chordal distance. Fifth, we propose a new hybrid MU-MISO system which has the advantages of the two MU-MIMO schemes, which are ZFBF and PU2RC simultaneously, and analyze the sum-rate performance and the quantization error of the hybrid scheme. The analysis shows how the number of feedback bits, the number of users, and the signal power affect the sum-rate. The throughput scaling laws are also derived in the high and the medium SNR regimes. We also propose a new codebook design scheme for the proposed hybrid MU-MISO system, which has hierarchical structure and thus it acheives low complexity.Contents Abstract i Contents iv List of Figures v List of Tables vi Chapter 1 Introduction 1 1.1 Scope and Organization . . . 6 Chapter 2 Multiuser MIMO User Selection Based on Chordal Distance . . . . . . .9 2.0.1 Block Diagonalization . . . . . . . . . 10 2.0.2 Chordal Distance . . . . . . . . . . . .13 2.1 LOWCOMPLEXITY SCHEDULING ALGORITHM . . . .15 2.1.1 Power Allocation . . . . . . . . . . . .15 2.1.2 Chordal Distance based MU-MIMO Scheduling Algorithm . . . . . . . . . . . . 16 2.2 COMPUTATIONAL COMPLEXITY ANALYSIS . . . . 18 2.2.1 Optimal Scheduling . . . . . . . . . . 19 2.2.2 Suboptimal Scheduling Algorithm . . . . 20 2.2.3 Chordal Distance based Scheduling Algorithm . . . . . . . . . . . . . . 21 2.3 Simulation Results . . . . . . . . . . 22 2.4 Summary . . . . . . . . . . . . . . . . 25 Chapter 3 Determinant Based Multiuser MIMO Scheduling with Reduced Pilot Overhead 27 3.1 SYSTEM MODEL. . . . . . . . . . . . . . .27 3.2 Determinant Based Multiuser MIMO Scheduling Algorithm . . . . . . . . . . . . . . . . . . . 28 3.2.1 Precoding Matrix . . . . . . . . . . . 28 3.2.2 Power Allocation . . . . . . . . . . . .30 3.2.3 Low Complexity MU-MIMO Scheduling Algorithm . .30 3.3 Computational Complexity Analysis . . . . . . . 36 3.3.1 Optimal Scheduling Algorithm . . . . . . . . .36 3.3.2 Suboptimal Scheduling Algorithm . . . . . . . 37 3.3.3 Determinant based Scheduling Algorithm . . . 37 3.4 Low Overhead Pilot Design for Block Diagonalization . . . . . . . . . . . . 39 3.5 Simulation Results . . . . . . . . . . .43 3.6 Summary . . . . . . . . . . . . . . . 47 Chapter 4 BER Based Multiuser MIMO Scheduling with Linear Precoding and Power Allocation 49 4.1 SYSTEM MODEL. . . . . . . . . . . . . . .50 4.2 POWER ALLOCATION ALGORITHMS . . . . . . .53 4.2.1 BER Minimization with Fixed Rate . . . 53 4.2.2 Throughput Maximization with Target BER . . 56 4.3 MULTIUSER MIMO SCHEDULING ALGORITHMS BASED ON BER . . . . . . 60 4.3.1 BER based Scheduling Algorithm . . . . . 60 4.3.2 Low Complexity BER based Scheduling Algorithm 65 4.4 Computational Complexity Analysis . . . . .71 4.4.1 BER based Multiuser MIMO Scheduling . . . .72 4.4.2 Low Complexity BER based Multiuser MIMO Scheduling . . . . . . . . . . . . . 74 4.4.3 Capacity based Multiuser MIMO Scheduling . . 76 4.5 Simulation Results . . . . . . . . . . 76 4.6 Summary . . . . . . . . . . . . . . . .84 Chapter 5 Regenerative Hierarchical Codebooks for Limited Channel Feedback in MIMO Systems 87 5.1 The existing codebooks. . . . . . . . . 88 5.1.1 Grassmannian Codebook . . . . . . . . 88 5.1.2 LBG algorithm . . . . . . . . . . . . 89 5.2 Hierarchical Codebook Design . . . . . 92 5.2.1 Self-regenerative Codebook Design for an i.i.d. channel . . . . . . . . . . . . . . . . 92 5.2.2 Hierarchical codebook design based on codebook mapping. . . . . . . . . . . . . 96 5.2.3 Codebook Design for Time Correlated Channel . 99 5.3 Performance Analysis . . . . . . . . . .102 5.4 Simulation Results . . . . . . . . . . .106 5.5 Summary . . . . . . . . . . . . . . . . 110 Chapter 6 Hybrid Multiuser MISO Scheduling with Limited Feedback using Hierarchical Codebooks 113 6.1 SYSTEM OVERVIEW . . . . . . . . . . 114 6.1.1 Zero-Forcing Beamforming. . . . . 116 6.1.2 Per User Unitary Rate Control (PU2RC) . .117 6.2 CODEBOOK DESIGN . . . . . . . . . . . . 120 6.3 A HYBRID MU-MISO SYSTEM WITH LIMITED FEEDBACK . 124 6.3.1 Feedback Scheme. . . . . . . . . . . . .124 6.3.2 User Selection . . . . . . . . . . . . .129 6.4 Performance Analysis of the proposed system 133 6.4.1 Quantization Error . . . . . . . . . . 133 6.4.2 High SNR or Interference-limited Regime . 136 6.4.3 Medium SNR Regime . . . . . . . . . 140 6.4.4 How to Select the Spherical Cap Size . .143 6.5 Simulation Results . . . . . . . . . . .145 6.6 Summary . . . . . . . . . . . . . . . . 153 Chapter 7 Conclusions 155 Bibliography 161 Abstract in Korean 168Docto
    corecore