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    Multiuser Scheduling and Hierarchical Codebook Design Techniques for MIMO Systems

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    ํ•™์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
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