63 research outputs found

    A Study Of Cooperative Spectrum Sharing Schemes For Internet Of Things Systems

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    The Internet of Things (IoT) has gained much attention in recent years with the massive increase in the number of connected devices. Cognitive Machine-to-Machine (CM2M) communications is a hot research topic in which a cognitive dimension allows M2M networks to overcome the challenges of spectrum scarcity, interference, and green requirements. In this paper, we propose a Generalized Cooperative Spectrum Sharing (GCSS) scheme for M2M communication. Cooperation extends the coverage of wireless networks as well as increasing their throughput while reducing the energy consumption of the connected low power devices. We study the outage performance of the proposed GCSS scheme for M2M system and derive exact expressions for the outage probability. We also analyze the effect of varying transmission powers on the performance of the system

    A Hop-by-Hop Relay Selection Strategy in Multi-Hop Cognitive Relay Networks

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    In this paper, a hop-by-hop relay selection strategy for multi-hop underlay cognitive relay networks (CRNs) is proposed. In each stage, relays that successfully decode the message from previous hop form a decoding set. Taking both maximum transmit power and maximum interference constraints into consideration, the relay in the decoding set which has the largest number of channels with an acceptable signal-to-noise ratio (SNR) level to the relays in the next stage is selected for retransmission. Therefore, relay selection in each stage only relies on channel state information (CSI) of the channels in that stage and does not require the CSI of any other stage. We analyze the performance of the proposed strategy in terms of endto-end outage probability and throughput, and show that the results match those obtained from simulation closely. Moreover, we derive the asymptotic end-to-end outage probability of the proposed strategy when there is no upper bound on transmittersโ€™ power. We compare this strategy to other hop-by-hop strategies that have appeared recently in the literature and show that this strategy has the best performance in terms of outage probability and throughput. Finally it is shown that the outage probability and throughput of the proposed strategy are very close to that of exhaustive strategy which provides a lower bound for outage probability and an upper bound for throughput of all path selection strategies

    Proactive and Reactive DF Relaying for Cognitive Network with Multiple Primary Users

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    In this paper, the outage performance of a cognitive radio network with a pair of secondary transmitter and receiver is investigated in the presence multiple primary users over a number of licensed frequency band and multiple secondary relays (SRs). Two decode and forward (DF) schemes are considered for the relays, namely proactive and reactive DF schemes. An adaptive power allocation scheme for secondary transmitter and secondary relays is formulated under the joint constraints of the primary user outage and peak transmit power of the secondary users. Based on these strategies, analytical expressions for the outage probability of proactive and reactive DF schemes are derived. More precisely, our results demonstrate the impact of number of the active primary users (PUs) over a number of available licensed frequency bands on the outage performance of secondary network. Further, it is observed that the performance of the secondary network can be improved by extending the bandwidth of the primary users

    ๋ฌด์„  ์ค‘๊ณ„ ๋„คํŠธ์›Œํฌ์—์„œ ์‹ ํ˜ธ๋Œ€์žก์Œ๋น„์˜ ๋ˆ„์ ๋ถ„ํฌํ•จ์ˆ˜ ๊ธฐ๋ฐ˜ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์˜ ์„ฑ๋Šฅ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2015. 8. ์ด์žฌํ™.๋ฌด์„  ์ค‘๊ณ„ ๊ธฐ์ˆ ์€ ์ฐจ์„ธ๋Œ€ ๋ฌด์„ ํ†ต์‹  ์‹œ์Šคํ…œ์—์„œ ์š”๊ตฌ๋˜๋Š” ๋†’์€ ์„œ๋น„์Šค ํ’ˆ์งˆ ๋ฐ ๋ฐ์ดํ„ฐ ์ „์†ก๋ฅ  ๋‹ฌ์„ฑ์„ ์œ„ํ•ด ๊ณ ๋ ค๋˜๊ณ  ์žˆ๋Š” ๋Œ€ํ‘œ์ ์ธ ๊ธฐ์ˆ  ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ฌด์„  ์ค‘๊ณ„ ๊ธฐ์ˆ ์ด ๊ฐ–๊ณ  ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์žฅ์ ์œผ๋กœ ์ธํ•ด ํ˜„์žฌ๊นŒ์ง€ IEEE 802.16j ๋ฐ 3GPP LTE-Advanced ๋“ฑ์˜ ๋ฌด์„ ํ†ต์‹  ์‹œ์Šคํ…œ ํ‘œ์ค€์— ๋ฐ˜์˜๋˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. ์‹ค์งˆ์ ์œผ๋กœ ๋‘ ๋…ธ๋“œ ์‚ฌ์ด ์ฑ„๋„์˜ ํ†ต๊ณ„์  ํŠน์„ฑ์€ ๊ทธ๋“ค์˜ ์œ„์น˜์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง€๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ ์ฑ„๋„๋“ค์˜ ํ†ต๊ณ„์  ํŠน์„ฑ์€ ์„œ๋กœ ๋™์ผํ•˜์ง€ ์•Š๋‹ค. ๊ฐ ์ฑ„๋„๋“ค์˜ ํ†ต๊ณ„์  ํŠน์„ฑ์ด ๋™์ผํ•˜์ง€ ์•Š์„ ๋•Œ, ๋ฌด์„  ์ค‘๊ณ„ ๊ธฐ์ˆ ์—์„œ ๊ฐ€์žฅ ์œ ์šฉํ•œ ๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์€ ํŠน์ • ์ค‘๊ณ„๊ธฐ๋“ค์ด ๋” ์ž์ฃผ ์„ ํƒ๋˜๋Š” ๋“ฑ์˜ ๊ณต์ •์„ฑ ๋ฌธ์ œ๋ฅผ ์œ ๋ฐœ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ, ์ด ๋ฌธ์ œ๋Š” ์ œํ•œ๋œ ๋ฐฐํ„ฐ๋ฆฌ๋ฅผ ๊ฐ€์ง„ ์ค‘๊ณ„๊ธฐ๋“ค๋กœ ๊ตฌ์„ฑ๋œ ๋„คํŠธ์›Œํฌ์—์„œ ๋„คํŠธ์›Œํฌ์˜ ์ˆ˜๋ช…์„ ์ค„์ด๊ฒŒ ํ•˜๋Š” ์š”์ธ์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ๋„คํŠธ์›Œํฌ์—์„œ๋Š” ์‚ฌ์šฉ์ž๋“ค์˜ ํ†ต์‹  ์‹ ๋ขฐ๋„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์ค‘๊ณ„๊ธฐ์—์„œ์˜ ์„ ํƒ ๊ณต์ •์„ฑ๋„ ํ•จ๊ป˜ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฌด์„  ์ค‘๊ณ„ ๋„คํŠธ์›Œํฌ์—์„œ ์‚ฌ์šฉ์ž๋“ค์˜ ํ†ต์‹  ์‹ ๋ขฐ๋„์™€ ์ค‘๊ณ„๊ธฐ ๊ฐ„์˜ ์„ ํƒ ๊ณต์ •์„ฑ์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜์‹  ์‹ ํ˜ธ๋Œ€์žก์Œ๋น„์˜ ๋ˆ„์ ๋ถ„ํฌํ•จ์ˆ˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฃผ์š”ํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋จผ์ €, ๋‚˜์นด๊ฐ€๋ฏธ-m ํŽ˜์ด๋”ฉ ์ฑ„๋„ ํ™˜๊ฒฝ์„ ๊ฐ€์ง„ ์ผ๋ฐฉํ–ฅ ์ค‘๊ณ„ ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ํ”„๋กœ์•กํ‹ฐ๋ธŒ(proactive) ๋ฐ ๋ฆฌ์•กํ‹ฐ๋ธŒ(reactive) ๋ฐฉ์‹์˜ ์ˆ˜์‹  ์‹ ํ˜ธ๋Œ€์žก์Œ๋น„ ๋ˆ„์ ๋ถ„ํฌํ•จ์ˆ˜ ๊ธฐ๋ฐ˜ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ๊ฐ์˜ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์„ ์œ„ํ•ด ์ค‘๊ณ„๊ธฐ ์„ ํƒ ํ™•๋ฅ ์„ ์œ ๋„ํ•˜์—ฌ ์ œ์•ˆ๋œ ๊ฐ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•๋“ค์˜ ํ‰๊ท  ์ค‘๊ณ„๊ธฐ ๊ณต์ •์„ฑ์„ ๋ถ„์„ํ•œ๋‹ค. ๋˜ํ•œ ๊ฐ ์„ ํƒ ๊ธฐ๋ฒ•์— ๋Œ€ํ•œ ๋ถˆ๋Šฅ ํ™•๋ฅ ์„ ์ˆ˜์‹์œผ๋กœ ์œ ๋„ํ•˜๊ณ , ์œ ๋„ํ•œ ๋ถˆ๋Šฅ ํ™•๋ฅ ์„ ์ ๊ทผ์  ํ‘œํ˜„์œผ๋กœ ๋‚˜ํƒ€๋‚ด์–ด ๊ฐ ๊ธฐ๋ฒ•๋“ค์ด ์–ป์„ ์ˆ˜ ์žˆ๋Š” ๋‹ค์ด๋ฒ„์‹œํ‹ฐ ์ฐจ์ˆ˜๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ๋ชจ์˜์‹คํ—˜์„ ํ†ตํ•ด ์–ป์–ด์ง„ ํ‰๊ท  ์ค‘๊ณ„๊ธฐ ๊ณต์ •์„ฑ๊ณผ ๋ถˆ๋Šฅ ํ™•๋ฅ ์ด ์œ ๋„ํ•œ ํ‰๊ท  ์ค‘๊ณ„๊ธฐ ๊ณต์ •์„ฑ ๋ฐ ๋ถˆ๋Šฅ ํ™•๋ฅ  ๊ฐ’๊ณผ ์ผ์น˜ํ•จ์„ ํ™•์ธํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์ด ์ค‘๊ณ„๊ธฐ๋“ค ์‚ฌ์ด์— ๊ณต์ •์„ฑ์„ ์™„๋ฒฝํ•˜๊ฒŒ ๋ณด์žฅํ•˜๊ณ  ๋„คํŠธ์›Œํฌ ์ˆ˜๋ช…์„ ์ฆ๊ฐ€์‹œํ‚ค๋ฉฐ, ๋‹ค์ด๋ฒ„์‹œํ‹ฐ ์ฐจ์ˆ˜๊ฐ€ ์ค‘๊ณ„๊ธฐ์˜ ์ˆ˜์™€ ํŽ˜์ด๋”ฉ ํŒŒ๋ผ๋ฏธํ„ฐ m ๊ฐ’์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง์„ ํ™•์ธํ•œ๋‹ค. ๋‘˜์งธ, ๋‚˜์นด๊ฐ€๋ฏธ-m ํŽ˜์ด๋”ฉ ์ฑ„๋„ ํ™˜๊ฒฝ์„ ๊ฐ€์ง„ ์–‘๋ฐฉํ–ฅ ์ค‘๊ณ„ ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ํ”„๋กœ์•กํ‹ฐ๋ธŒ ๋ฐ ๋ฆฌ์•กํ‹ฐ๋ธŒ ๋ฐฉ์‹์˜ ์ˆ˜์‹  ์‹ ํ˜ธ๋Œ€์žก์Œ๋น„ ๋ˆ„์ ๋ถ„ํฌํ•จ์ˆ˜ ๊ธฐ๋ฐ˜ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ํ”„๋กœ์•กํ‹ฐ๋ธŒ ๋ฐฉ์‹์˜ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์— ๋Œ€ํ•ด์„œ๋Š” ์ •ํ™•ํ•œ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ํ™•๋ฅ ์˜ ์œ ๋„๋ฅผ ํ†ตํ•ด ํ‰๊ท  ์ค‘๊ณ„๊ธฐ ๊ณต์ •์„ฑ์„ ๋ถ„์„ํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฆฌ์•กํ‹ฐ๋ธŒ ๋ฐฉ์‹์˜ ์ค‘๊ณ„๊ธฐ ์„ ํƒ ๊ธฐ๋ฒ•์— ๋Œ€ํ•ด์„œ๋Š” ์ค‘๊ณ„๊ธฐ ์„ ํƒ ํ™•๋ฅ ์˜ ์ ๋ถ„ ๋ฐ ๊ทผ์‚ฌ ํ‘œํ˜„์„ ์œ ๋„ํ•˜์—ฌ ํ‰๊ท  ์ค‘๊ณ„๊ธฐ ๊ณต์ •์„ฑ์„ ๋ถ„์„ํ•œ๋‹ค. ๋˜ํ•œ ๊ฐ ์„ ํƒ ๊ธฐ๋ฒ•์— ๋Œ€ํ•œ ๋ถˆ๋Šฅ ํ™•๋ฅ ์„ ์ˆ˜์‹์œผ๋กœ ์œ ๋„ํ•˜๊ณ , ์œ ๋„ํ•œ ๋ถˆ๋Šฅ ํ™•๋ฅ ์„ ์ ๊ทผ์  ํ‘œํ˜„์œผ๋กœ ๋‚˜ํƒ€๋‚ด์–ด ๊ฐ ๊ธฐ๋ฒ•๋“ค์ด ์–ป์„ ์ˆ˜ ์žˆ๋Š” ๋‹ค์ด๋ฒ„์‹œํ‹ฐ ์ฐจ์ˆ˜๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ๋ชจ์˜์‹คํ—˜์„ ํ†ตํ•ด ์–ป์–ด์ง„ ํ‰๊ท  ์ค‘๊ณ„๊ธฐ ๊ณต์ •์„ฑ๊ณผ ๋ถˆ๋Šฅ ํ™•๋ฅ ์ด ์œ ๋„ํ•œ ํ‰๊ท  ์ค‘๊ณ„๊ธฐ ๊ณต์ •์„ฑ ๋ฐ ๋ถˆ๋Šฅ ํ™•๋ฅ  ๊ฐ’๊ณผ ์ผ์น˜ํ•จ์„ ํ™•์ธํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์ด ์ค‘๊ณ„๊ธฐ๋“ค ์‚ฌ์ด์— ๊ณต์ •์„ฑ์„ ์™„๋ฒฝํ•˜๊ฒŒ ๋ณด์žฅํ•˜๊ณ  ๋„คํŠธ์›Œํฌ ์ˆ˜๋ช…์„ ์ฆ๊ฐ€์‹œํ‚ค๋ฉฐ, ๋‹ค์ด๋ฒ„์‹œํ‹ฐ ์ฐจ์ˆ˜๊ฐ€ ์ค‘๊ณ„๊ธฐ์˜ ์ˆ˜์™€ ํŽ˜์ด๋”ฉ ํŒŒ๋ผ๋ฏธํ„ฐ m ๊ฐ’์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง์„ ํ™•์ธํ•œ๋‹ค.Wireless relay technology is one of the most promising technologies for the future communication systems which provide coverage extension and better quality of service (QoS) such as higher data rate and lower outage probability with few excessive network loads. Due to its advantages, it has been adopted in wireless standards such as IEEE 802.16j and 3GPP LTE-Advanced. In practice, since statistics of the channel between any two nodes vary depending on their locations, they are not identical which means that channels can experience different fading. When statistics of the channel are not identical, relay selection, which is one of the most useful techniques for wireless relay technology, can cause fairness problem that particular relays are selected more frequently than other relays. Especially, this problem can cause reduction of lifetime in the network with multiple relays having limited battery power. In this network, it is needed to focus on selection fairness for relays as well as reliability at end-users. In this dissertation, to focus on both selection fairness for relays and reliability at end-users, we propose novel relay selection schemes based on cumulative distribution functions (CDFs) of signal-to-noise ratios (SNRs) in wireless relay networks. The dissertation consists of two main results. First, we propose the proactive and the reactive relay selection schemes based on CDFs of SNRs for one-way relay networks over Nakagami-m fading channels. If a relay is selected before the source transmission, it is called as proactive relay selection. Otherwise, if a relay is selected after the source transmission, it is called as reactive relay selection. For both the proactive and the reactive relay selection schemes, we analyze average relay fairness by deriving relay selection probability. For the proactive relay selection scheme, we obtain diversity order by deriving the integral and asymptotic expressions for outage probability. Also, for the reactive relay selection scheme, we obtain diversity order by deriving the exact closed-form and asymptotic expressions for outage probability. Numerical results show that the analytical results of the proposed schemes match the simulation results well. It is shown that the proposed schemes guarantee strict fairness among relays and extend network lifetime. Also, it is shown that diversity order depends on the number of relays and fading severity parameters. Second, we propose the proactive and the reactive relay selection schemes based on CDFs of SNRs for two-way relay networks over Nakagami-m fading channels. For the proactive relay selection scheme, we analyze average relay fairness by deriving relay selection probability. Also, we analyze diversity order by deriving the integral and asymptotic expressions for outage probability. For the reactive relay selection scheme, we analyze average relay fairness by deriving the integral and asymptotic expressions for relay selection probability. Also, we obtain diversity order by deriving the asymptotic expression for outage probability. Numerical results show that the analytical results of the proposed schemes match the simulation results well. It is shown that the proposed schemes guarantee strict fairness among relays and extend network lifetime. Also, it is shown that diversity order depends on the number of relays and fading severity parameters.Contents Abstract i 1 Introduction 1 1.1 Background and Related Work . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Wireless Relay Technology . . . . . . . . . . . . . . . . . . . . 3 1.2 Outline of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Relay Selection Based on CDFs of SNRs for One-Way Relay Networks 14 2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1.1 Proactive CDF-Based Relay Selection . . . . . . . . . . . . . 19 2.1.2 Reactive CDF-Based Relay Selection . . . . . . . . . . . . . . 20 2.2 Performance Analysis of Proactive CDF-Based Relay Selection . . . . 22 2.2.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 22 2.2.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 27 2.3 Performance Analysis of Reactive CDF-Based Relay Selection . . . . 34 2.3.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 34 2.3.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 36 2.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.4.1 Average Relay Fairness . . . . . . . . . . . . . . . . . . . . . . 39 2.4.2 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.4.3 Outage Probability . . . . . . . . . . . . . . . . . . . . . . . . 53 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3 Relay Selection Based on CDFs of SNRs for Two-Way Relay Networks 66 3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.1.1 Proactive CDF-based Relay Selection . . . . . . . . . . . . . . 68 3.1.2 Reactive CDF-based Relay Selection . . . . . . . . . . . . . . 72 3.2 Performance Analysis of Proactive CDF-Based Relay Selection . . . . 73 3.2.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 73 3.2.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 74 3.3 Performance Analysis of Reactive CDF-Based Relay Selection . . . . 82 3.3.1 Average Relay Fairness Anlaysis . . . . . . . . . . . . . . . . . 82 3.3.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 86 3.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.1 Average Relay Fairness . . . . . . . . . . . . . . . . . . . . . . 89 3.4.2 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . 100 3.4.3 Outage Probability . . . . . . . . . . . . . . . . . . . . . . . . 105 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 4 Conclusion 116 4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 4.2 Possible Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 4.2.1 Device-to-Device (D2D) Communications . . . . . . . . . . . 118 4.2.2 Low Power Body Sensor Networks . . . . . . . . . . . . . . . 120 4.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Bibliography 122 Korean Abstract 139Docto

    Relay Selection Strategies for Multi-hop Cooperative Networks

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    In this dissertation we consider several relay selection strategies for multi-hop cooperative networks. The relay selection strategies we propose do not require a central controller (CC). Instead, the relay selection is on a hop-by-hop basis. As such, these strategies can be implemented in a distributed manner. Therefore, increasing the number of hops in the network would not increase the complexity or time consumed for the relay selection procedure of each hop. We first investigate the performance of a hop-by-hop relay selection strategy for multi-hop decode-and-forward (DF) cooperative networks. In each relay cluster, relays that successfully receive and decode the message from the previous hop form a decoding set for relaying, and the relay which has the highest signal-to-noise ratio (SNR) link to the next hop is then selected for retransmission. We analyze the performance of this method in terms of end-to-end outage probability, and we derive approximations for the ergodic capacity and the effective ergodic capacity of this strategy. Next we propose a novel hop-by-hop relay selection strategy where the relay in the decoding set with the largest number of ``good\u27\u27 channels to the next stage is selected for retransmission. We analyze the performance of this method in terms of end-to-end outage probability in the case of perfect and imperfect channel state information (CSI). We also investigate relay selection strategies in underlay spectrum sharing cognitive relay networks. We consider a two-hop DF cognitive relay network with a constraint on the interference to the primary user. The outage probability of the secondary user and the interference probability at the primary user are analyzed under imperfect CSI scenario. Finally we introduce a hop-by-hop relay selection strategy for underlay spectrum sharing multi-hop relay networks. Relay selection in each stage is only based on the CSI in that hop. It is shown that in terms of outage probability, the performance of this method is nearly optimal
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