3 research outputs found
무μ μ€κ³ λ€νΈμν¬μμ μ νΈλμ‘μλΉμ λμ λΆν¬ν¨μ κΈ°λ° μ€κ³κΈ° μ ν κΈ°λ²μ μ±λ₯ λΆμ
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2015. 8. μ΄μ¬ν.무μ μ€κ³ κΈ°μ μ μ°¨μΈλ 무μ ν΅μ μμ€ν
μμ μꡬλλ λμ μλΉμ€ νμ§ λ° λ°μ΄ν° μ μ‘λ₯ λ¬μ±μ μν΄ κ³ λ €λκ³ μλ λνμ μΈ κΈ°μ μ€ νλμ΄λ€. 무μ μ€κ³ κΈ°μ μ΄ κ°κ³ μλ λ€μν μ₯μ μΌλ‘ μΈν΄ νμ¬κΉμ§ IEEE 802.16j λ° 3GPP LTE-Advanced λ±μ 무μ ν΅μ μμ€ν
νμ€μ λ°μλκΈ°λ νμλ€.
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Έλ μ¬μ΄ μ±λμ ν΅κ³μ νΉμ±μ κ·Έλ€μ μμΉμ λ°λΌ λ¬λΌμ§κΈ° λλ¬Έμ κ° μ±λλ€μ ν΅κ³μ νΉμ±μ μλ‘ λμΌνμ§ μλ€. κ° μ±λλ€μ ν΅κ³μ νΉμ±μ΄ λμΌνμ§ μμ λ, 무μ μ€κ³ κΈ°μ μμ κ°μ₯ μ μ©ν κΈ°λ² μ€ νλμΈ μ€κ³κΈ° μ ν κΈ°λ²μ νΉμ μ€κ³κΈ°λ€μ΄ λ μμ£Ό μ νλλ λ±μ 곡μ μ± λ¬Έμ λ₯Ό μ λ°μν¬ μ μλ€. νΉν, μ΄ λ¬Έμ λ μ νλ λ°°ν°λ¦¬λ₯Ό κ°μ§ μ€κ³κΈ°λ€λ‘ ꡬμ±λ λ€νΈμν¬μμ λ€νΈμν¬μ μλͺ
μ μ€μ΄κ² νλ μμΈμ΄ λ μ μλ€. λ°λΌμ μ΄λ¬ν λ€νΈμν¬μμλ μ¬μ©μλ€μ ν΅μ μ λ’°λ λΏλ§ μλλΌ, μ€κ³κΈ°μμμ μ ν 곡μ μ±λ ν¨κ» κ³ λ €ν νμκ° μλ€.
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Όλ¬Έμμλ 무μ μ€κ³ λ€νΈμν¬μμ μ¬μ©μλ€μ ν΅μ μ λ’°λμ μ€κ³κΈ° κ°μ μ ν 곡μ μ±μ ν¨κ» κ³ λ €νκΈ° μν΄ μμ μ νΈλμ‘μλΉμ λμ λΆν¬ν¨μλ₯Ό κΈ°λ°μΌλ‘ νλ μλ‘μ΄ μ€κ³κΈ° μ ν κΈ°λ²μ μ μνλ€. μ£Όμν μ°κ΅¬ κ²°κ³Όλ λ€μκ³Ό κ°λ€.
λ¨Όμ , λμΉ΄κ°λ―Έ-m νμ΄λ© μ±λ νκ²½μ κ°μ§ μΌλ°©ν₯ μ€κ³ λ€νΈμν¬λ₯Ό μν νλ‘μ‘ν°λΈ(proactive) λ° λ¦¬μ‘ν°λΈ(reactive) λ°©μμ μμ μ νΈλμ‘μλΉ λμ λΆν¬ν¨μ κΈ°λ° μ€κ³κΈ° μ ν κΈ°λ²μ μ μνλ€. κ°κ°μ μ€κ³κΈ° μ ν κΈ°λ²μ μν΄ μ€κ³κΈ° μ ν νλ₯ μ μ λνμ¬ μ μλ κ° μ€κ³κΈ° μ ν κΈ°λ²λ€μ νκ· μ€κ³κΈ° 곡μ μ±μ λΆμνλ€. λν κ° μ ν κΈ°λ²μ λν λΆλ₯ νλ₯ μ μμμΌλ‘ μ λνκ³ , μ λν λΆλ₯ νλ₯ μ μ κ·Όμ ννμΌλ‘ λνλ΄μ΄ κ° κΈ°λ²λ€μ΄ μ»μ μ μλ λ€μ΄λ²μν° μ°¨μλ₯Ό λΆμνλ€. λͺ¨μμ€νμ ν΅ν΄ μ»μ΄μ§ νκ· μ€κ³κΈ° 곡μ μ±κ³Ό λΆλ₯ νλ₯ μ΄ μ λν νκ· μ€κ³κΈ° 곡μ μ± λ° λΆλ₯ νλ₯ κ°κ³Ό μΌμΉν¨μ νμΈνλ€. κ·Έλ¦¬κ³ μ μλ κΈ°λ²μ΄ μ€κ³κΈ°λ€ μ¬μ΄μ 곡μ μ±μ μλ²½νκ² λ³΄μ₯νκ³ λ€νΈμν¬ μλͺ
μ μ¦κ°μν€λ©°, λ€μ΄λ²μν° μ°¨μκ° μ€κ³κΈ°μ μμ νμ΄λ© νλΌλ―Έν° m κ°μ λ°λΌ λ¬λΌμ§μ νμΈνλ€.
λμ§Έ, λμΉ΄κ°λ―Έ-m νμ΄λ© μ±λ νκ²½μ κ°μ§ μλ°©ν₯ μ€κ³ λ€νΈμν¬λ₯Ό μν νλ‘μ‘ν°λΈ λ° λ¦¬μ‘ν°λΈ λ°©μμ μμ μ νΈλμ‘μλΉ λμ λΆν¬ν¨μ κΈ°λ° μ€κ³κΈ° μ ν κΈ°λ²μ μ μνλ€. μ μλ νλ‘μ‘ν°λΈ λ°©μμ μ€κ³κΈ° μ ν κΈ°λ²μ λν΄μλ μ νν μ€κ³κΈ° μ ν νλ₯ μ μ λλ₯Ό ν΅ν΄ νκ· μ€κ³κΈ° 곡μ μ±μ λΆμνλ€. μ μλ 리μ‘ν°λΈ λ°©μμ μ€κ³κΈ° μ ν κΈ°λ²μ λν΄μλ μ€κ³κΈ° μ ν νλ₯ μ μ λΆ λ° κ·Όμ¬ ννμ μ λνμ¬ νκ· μ€κ³κΈ° 곡μ μ±μ λΆμνλ€. λν κ° μ ν κΈ°λ²μ λν λΆλ₯ νλ₯ μ μμμΌλ‘ μ λνκ³ , μ λν λΆλ₯ νλ₯ μ μ κ·Όμ ννμΌλ‘ λνλ΄μ΄ κ° κΈ°λ²λ€μ΄ μ»μ μ μλ λ€μ΄λ²μν° μ°¨μλ₯Ό λΆμνλ€. λͺ¨μμ€νμ ν΅ν΄ μ»μ΄μ§ νκ· μ€κ³κΈ° 곡μ μ±κ³Ό λΆλ₯ νλ₯ μ΄ μ λν νκ· μ€κ³κΈ° 곡μ μ± λ° λΆλ₯ νλ₯ κ°κ³Ό μΌμΉν¨μ νμΈνλ€. κ·Έλ¦¬κ³ μ μλ κΈ°λ²μ΄ μ€κ³κΈ°λ€ μ¬μ΄μ 곡μ μ±μ μλ²½νκ² λ³΄μ₯νκ³ λ€νΈμν¬ μλͺ
μ μ¦κ°μν€λ©°, λ€μ΄λ²μν° μ°¨μκ° μ€κ³κΈ°μ μμ νμ΄λ© νλΌλ―Έν° m κ°μ λ°λΌ λ¬λΌμ§μ νμΈνλ€.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
Multi-way relay networks: characterization, performance analysis and transmission scheme design
Multi-way relay networks (MWRNs) are a growing research area in the field of relay
based wireless networks. Such networks provide a pathway for solving the ever in-
creasing demand for higher data rate and spectral efficiency in a general multi-user
scenario. MWRNs have potential applications in video conferencing, file sharing in
a social network, as well as satellite networks and sensor networks. Recent research
on MWRNs focuses on efficient transmission protocol design by harnessing different
network coding schemes, higher dimensional structured codes and advanced relaying
protocols. However, the existing research misses out the characterization and analysis
of practical issues that influence the performance of MWRNs. Moreover, the existing
transmission schemes suffer some significant limitations, that need to be solved for
maximizing the benefits of MWRNs.
In this thesis, we investigate the practical issues that critically influence the perfor-
mance of a MWRN and propose solutions that can outperform existing schemes. To
be specific, we characterize error propagation phenomenon for additive white Gaus-
sian noise (AWGN) and fading channels with functional decode and forward (FDF) and
amplify and forward (AF) relaying protocols, propose a new pairing scheme that out-
performs the existing schemes for lattice coded FDF MWRNs in terms of the achievable
rate and error performance and finally, analyze the impact of imperfect channel state
information (CSI) and optimum power allocation on MWRNs.
At first, we analyze the error performance of FDF and AF MWRNs with pair-
wise transmission using binary phase shift keying (BPSK) modulation in AWGN and
Rayleigh fading channels. We quantify the possible error events in an L-user FDF or AF
MWRN and derive accurate asymptotic bounds on the probability for the general case
that a user incorrectly decodes the messages of exactly k (k β [1, L β 1]) other users. We
show that at high signal-to-noise ratio (SNR), the higher order error events (k β₯ 3) are less probable in AF MWRN, but all error events are equally probable in a FDF MWRN.
We derive the average BER of a user in a FDF or AF MWRN under high SNR conditions
and provide simulation results to verify them.
Next, we propose a novel user pairing scheme for lattice coded FDF MWRNs. Lattice
codes can achieve the capacity of AWGN channels and are used in digital communica-
tions as high-rate signal constellations. Our proposed pairing scheme selects a common
user with the best average channel gain and thus, allows it to positively contribute to
the overall system performance. Assuming lattice code based transmissions, we derive
upper bounds on the average common rate and the average sum rate with the proposed
pairing scheme. In addition, considering M-ary QAM with square constellation as a
special case of lattice codes, we derive asymptotic average symbol error rate (SER) of
the MWRN. We show that in terms of the achievable rates and error performance, the
proposed pairing scheme outperforms the existing pairing schemes under a wide range
of channel scenarios.
Finally, we investigate lattice coded FDF and AF MWRNs with imperfect CSI. Con-
sidering lattice codes of sufficiently large dimension, we obtain the bounds on the com-
mon rate and sum rate. In addition, considering M-ary quadrature amplitude mod-
ulation (QAM) with square constellations, we obtain expressions for the average SER
in FDF MWRNs. For AF MWRNs, considering BPSK modulation as the simplest case
of lattice codes, we obtain the average BER. Moreover, we obtain the optimum power
allocation coefficients to maximize the sum rate in AF MWRN. For both FDF and AF
relaying protocols, the average common rate and sum rate are decreasing functions of
the estimation error. The analysis shows that the error performance of a FDF MWRN
is an increasing function of both the channel estimation error and the number of users,
whereas, for AF MWRN, the error performance is an increasing function of only the
channel estimation error. Also, we show that to achieve the same sum rate in AF
MWRN, optimum power allocation requires 7 β 9 dB less power compared to equal
power allocation depending upon usersβ channel conditions