Relay Networks Nalin D. K. Jayakody and Khao D. Nguyen* Institute - - PowerPoint PPT Presentation

Transceiver Hardware Im Impairments in in Cognitive Relay Networks

Nalin D. K. Jayakodyⱡ and Khao D. Nguyen* ⱡInstitute of Computer Science, University of Tartu, ESTONIA *Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, JAPAN

Estonian CS Theory Days 2016, Tartu, Estonia 29th Jan 2016

Content

• Background: Cognitive Relay Networks
• Contribution/Motivation
• Transceiver Hardware Impairment
• System Model
• Soft Forwarding
• Calculation of LLR
• Simulation Results
• Summary

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• No direct wireless channel

Wir ireless relay transmission

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Relay node 1st phase 2nd phase Relay network

The data forwarding algorithm at the relay node is called the

Relay Protocol or Relay Scheme

Wir ireless rela lay transmission

• Relay node supports the wireless transmission even without a direct link.
• Relay also can improve the reliability of the transmission in case the direct

link is present.

• Two versions of the received data at B are combined with a data combing

algorithm, such as maximal combining.

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1st phase 2nd phase

Ampli lify fy and Forw rward & Decode and Forw rward

Amplify and Forward

• Constituting one of the simplest and most popular relaying methods, the signal received by the

relay is amplified, frequency translated and retransmitted.

• An important design issue related to transparent relaying protocols is the choice of amplification

factor in the relay, i.e. constant output power, fixed gain amplification. Decode and Forward

• Being the prominent counter protocol to the transparent AF protocol, DF detects the signal,

decodes it and re-encodes it prior to retransmission. A vast amount of different DF protocols exists today e.g., selective DF, Decode Amplify and Forward (DAF) etc.

• Such regeneration can include sample, demodulating, decoding, re-encoding, re-modulating etc.,

as well as any joint combination thereof.

Current Bandwid idth usage

• Consider two networks

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Network 1: Network 2: Relay network: no direct link Transmit via bandwidth 2 Transmit via bandwidth 1

Bandwid idth usage effic iciency

• Two networks are sharing same bandwidth?
• Primary network: licensed bandwidth  priority
• Secondary network: unlicensed bandwidth  limit

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Network 1: Network 2: Transmit via the same bandwidth Interference Cognitive relay network

Cognit itive rela lay network

• Secondary users (SUs) and primary users (PUs) transmit via the same

bandwidth.

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Limit the interferences

 Limit transmit power of the SU  Specified applications, such as sensor network, network for disaster area.

A cognitive relay network Deploy far from PUs

Cognit itive rela lay networks

• Transmit power limitation and low cost
• Distortions of transceiver will reduce performance
• Transceiver impairment effect should be accounted for analysis

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Limit the interferences A cognitive relay network

Contributions

• We portray soft information relaying (SIR) protocol for multi-hop cognitive relay networks.
• We provide some simulation results for (achievable) throughput and BER performance of the

network with SIR protocol under the impact of hardware impairments.

• For this purpose, an expression is derived for the soft noise variance and equivalent noise

variance to reflect the hardware impairments.

• Finally, we present simulation results which show benefits of the SIR scheme compared to hard

DF protocol.

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Why soft

ft in information relaying?

• The Amplify and Forward (AF) and Decode and Forward (DF) protocols suffer from noise

amplification and error propagation, respectively

• In order to combine the advantages of both AF and DF in relay networks, many strategies have

been proposed in which soft (reliability) information is transmitted to the destination; this idea is known as soft information relaying (SIR)

• SIR has been shown to be an effective solution which mitigates the propagation of relay decoding

errors to the destination

• As the destination decoder works in the probabilistic domain, the soft information relaying (SIR)

protocol complies with the decoder’s requirements

• It also improves the reliability of the relay received signal to the destination

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Transceiv iver hardware im impairment

• Main sources of impairment
• Phase noise
• IQ imbalance
• Nonlinearities
• Unify the impacts of transceiver impairments
• Introduce distortion at source and sink

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Each source cause different distortion

Distortion at source Distortion at sink

Transceiv iver Hardware Impair irments

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: Impairment level : AWGN Noise

Received signal model as in Mattaiou et al.: Simplified general model:

Aggregate impairment

Varia iance of

• f im

impairment-nois ise-distortion (tr (transmit power P P = = 1) 1) for dif ifference im impairments le levels ls 𝜆2 = [0.08; 0.1275; 0.175]

System Model

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ℎ𝑗 , 𝑕𝑘 − channel co-efficient 𝜃𝑙 ~ 𝒟𝒪 0, 𝑂0 , AWGN noise

※ 𝜆𝑠

2: Aggregate impairment at R

※ 𝜆𝑒

2: Aggregate impairment at D

※ 𝐽𝑄: Maximum transmit power ※ All channels are AWGN ※ Secondary user ※ Primary user 𝛿 = 𝐽𝑄 𝑂0

• Transmit power at S and R
• SNR:

𝑄

𝑇 = 𝑄 𝑆 = 𝐽𝑄

Soft information relaying protocol is applied at the relay with BPSK modulation

Relay Protocol

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Phase 1 Phase 2

Calculate soft-information of the received signal 𝑧𝑇𝑆

SR SD

Demod.

Hard decisi

• n

Mod. Soft Demod.

tanh(𝑀/ 2)

Mod. Soft BPSK

𝒛𝑻𝑺 𝒚𝑺

Soft In Information Calc lculation

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• 1. LLRs of the received signal
• 2. Calculate soft bits
• 3. The relationship between the soft symbol at

𝑦𝑆 and the correct 𝑦𝑆 symbol, is modeled in Li et al. as where is the soft noise random variable (we can estimate and mean 𝜈 and variance )

Soft Information Modeli ling

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• 4. From 3, we can model the received signal at D as

where the equivalent aggregate noise term of the received signal at D from R in the second timeslot. This equivalent noise distribution has zero mean and variance . where equals to .

• 5. The LLR of 𝑧𝑆𝐸 is approximated using the soft noise model as follows

Calc lculate LLRs at Destination

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• 6. LLR of 𝑧𝑇𝐸
• 7. LLR at D
• 8. Hard decision

Sim imulations

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Compare the performance of soft information relaying (SIR) and decode-and-forward (DF) using:

 Throughput  BER

Target

Parameter Value Transmit power 𝑄

𝑇 = 𝑄𝑆 = 1

Modulation BPSK Hardware impairment 𝜆𝑆

2 = 𝜆𝑆 2 = 𝜆2 ∈ [0,0.175]

Bandwidth 𝐶 = 1 (Hz) Noise variance 𝜏𝜃

2 = 1

Relay protocols DF, hard decision SIR, soft decision

Result lt – Achievable Throughput

Throughput performance of the network with soft information relaying when the bandwidth 𝐶 = 1 (Hz), noise variance 𝜏2 = 1 and 𝜆2 = [0, 0.08, 0.175, 0.1275]

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• 1. Ideal model: throughput

increases as the transmit power grows.

• 2. Impairment model: establish the

ceiling throughput  cannot increase to infinity.

• 3. The larger impairment levels,

the lower maximum throughput.

Result lts – BER performance

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• 1. SIR outperform DF protocol

with hard decision

• 2. BER performance of SIR scheme

with 𝜆2 = 0.175 approximately as good as DF with 𝜆2 = 0 and just under SIR scheme with 𝜆2 = 0.175  SIR protocol is efficient in improving system performance

BER performance for the SIR protocol in compared to DF protocol over AWGN channel for ideal transceiver and transceiver model with hardware impairment level 𝜆2 = 0.175

Future: Energy Harvestin ing

 Dual-source (DS)

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 Single-fixed source (SFS)

 Both A and B transmit RF signal to R in the energy harvesting phase  The harvested power at R is 𝐹𝐼  Only one B or A transmits RF signal to R in the energy harvesting phase  The harvested power at R is 𝐹𝐼

Conclusion

• Present a new results in soft information relay network assuming practical imperfect hardware
• Efficient in mitigation of the impact of hardware transceiver impairment compared with DF

relaying

• In particular, the BER of the SIR network with hardware impairment level 𝜆2 = 0.175 and the DF

protocol with perfect transceiver 𝜆2 = 0 are of the same parity.

• we confirm the fundamental limit of realistic transceiver hardware on the achievable throughput.

The maximum throughput is established (ceiling point) even when the transmit power increases to infinity.

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Thank you 

References

• Y. Li, B. Vucetic, T. F. Wong and M. Dohler, “Distributed turbo coding with soft information relaying

in multihop relay networks,” IEEE Journal on Sel. Areas in Comm., vol. 24, no. 11, pp. 2040–2050,

• Nov. 2006.
• T. Schenk, RF Imperfections in High-Rate Wireless Systems: Impact and Digital Compensation.

Springer Publishing, 2010.

• M. Matthaiou, A. Papadogiannis, E. Bjõrnson, and M. Debbah, “Two way relaying under the

presence of relay transceiver hardware impairments,” IEEE Commun. Lett., vol. 17, no. 6, pp. 1136–1139, June 2013.

Acknowledgement

This work is supported (in part) by the Norwegian-Estonian Research Cooperation Programme through the grant EMP133, by the Estonian Research Council through the research grants PUT405.