Transceiver Hardware Im Impairments in in Cognitive Relay Networks Nalin D. K. Jayakody ⱡ and Khao D. Nguyen* ⱡ Institute 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 29 th Jan 2016
Content • Background: Cognitive Relay Networks • Contribution/Motivation • Transceiver Hardware Impairment • System Model • Soft Forwarding • Calculation of LLR • Simulation Results • Summary 2
Wir ireless relay transmission • No direct wireless channel Relay node 1 st phase 2 nd phase The data forwarding algorithm at the relay node is called the Relay network Relay Protocol or Relay Scheme 3
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. 1 st phase 2 nd phase 4
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 Network 1: Transmit via bandwidth 1 Transmit via bandwidth 2 Network 2: Relay network: no direct link 6
Bandwid idth usage effic iciency • Two networks are sharing same bandwidth? • Primary network: licensed bandwidth priority • Secondary network: unlicensed bandwidth limit Network 1: Transmit via the same bandwidth Interference Network 2: Cognitive relay network 7
Cognit itive rela lay network • Secondary users (SUs) and primary users (PUs) transmit via the same bandwidth. Deploy far Limit transmit power of the SU from PUs Specified applications, such as sensor network, network for Limit the disaster area. interferences A cognitive relay network 8
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 Limit the interferences A cognitive relay network 9
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. 3
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 4
Transceiv iver hardware im impairment • Main sources of impairment • Phase noise • IQ imbalance Each source cause different distortion • Nonlinearities • Unify the impacts of transceiver impairments • Introduce distortion at source and sink Distortion at source Distortion at sink 12
Transceiv iver Hardware Impair irments : Impairment level : AWGN Noise Received signal model as in Mattaiou et al. : Simplified general model : Aggregate impairment 13
Varia iance of of im impairment-nois ise-distortion (tr (transmit power P P = = 1) 1) for dif ifference ls 𝜆 2 = [0.08; 0.1275; 0.175] im impairments le levels
System Model ℎ 𝑗 , 𝑘 − channel co-efficient 𝜃 𝑙 ~ 𝒟𝒪 0, 𝑂 0 , AWGN noise Soft information relaying protocol is applied at the relay with BPSK modulation Transmit power at S and R 2 : Aggregate impairment at R ※ 𝜆 𝑠 𝑄 𝑇 = 𝑄 𝑆 = 𝐽 𝑄 2 : Aggregate impairment at D ※ 𝜆 𝑒 ※ 𝐽 𝑄 : Maximum transmit power SNR: ※ All channels are AWGN 𝛿 = 𝐽 𝑄 ※ Secondary user 𝑂 0 ※ Primary user 15
Relay Protocol S R Phase 1 S D Calculate soft-information of the received signal 𝑧 𝑇𝑆 Phase 2 𝒛 𝑻𝑺 𝒚 𝑺 Hard tanh(𝑀/ Mod. Soft 2 ) decisi Mod. Soft Demod. Demod. BPSK on 16
Soft In Information Calc lculation 1. LLRs of the received signal 2. Calculate soft bits 3. The relationship between the soft symbol at 𝑦 𝑆 and the correct 𝑦 𝑆 symbol, is modeled in Li et al. as where is the soft noise random variable (we can estimate and mean 𝜈 and variance ) 17
Soft Information Modeli ling 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 𝑧 𝑆𝐸 is approximated using the soft noise model as follows 18
Calc lculate LLRs at Destination 6. LLR of 𝑧 𝑇𝐸 7. LLR at D 8. Hard decision 19
Sim imulations Parameter Value Transmit power 𝑄 𝑇 = 𝑄 𝑆 = 1 Modulation BPSK 2 = 𝜆 𝑆 2 = 𝜆 2 ∈ [0,0.175] Hardware impairment 𝜆 𝑆 Bandwidth 𝐶 = 1 (Hz) 2 = 1 Noise variance 𝜏 𝜃 Relay protocols DF, hard decision SIR, soft decision Compare the performance of soft information relaying (SIR) and decode-and-forward (DF) using: Target Throughput BER 20
Result lt – Achievable Throughput Throughput performance of the network with soft information relaying when the bandwidth 𝐶 = 1 (Hz), noise variance 𝜏 2 = 1 and 𝜆 2 = [0, 0.08, 0.175, 0.1275] 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. 21
Result lts – BER 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 𝜆 2 = 0.175 1. SIR outperform DF protocol with hard decision 2. BER performance of SIR scheme with 𝜆 2 = 0.175 approximately as good as DF with 𝜆 2 = 0 and just under SIR scheme with 𝜆 2 = 0.175 SIR protocol is efficient in improving system performance 22
Future: Energy Harvestin ing Dual-source (DS) Single-fixed source ( SFS ) Both A and B transmit RF signal Only one B or A transmits RF to R in the energy harvesting signal to R in the energy phase harvesting phase The harvested power at R is 𝐹 𝐼 The harvested power at R is 𝐹 𝐼 23
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 𝜆 2 = 0.175 and the DF protocol with perfect transceiver 𝜆 2 = 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. 24
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.
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