Armour, SMD

McGeehan, JP

Nordin, R

English

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Overcoming Self- Interference in SM-OFDMA with ESINR and Dynamic Subcarrier Allocation

For a single user with multiple receive antennas, co-antenna interference can be the dominant source of impairment in a Spatial Multiplex Orthogonal Frequency Division Multiple Access (SM-OFDMA) system, particularly when correlation exists between spatial sub-channels. By exploiting knowledge of the channel response and combining it with Dynamic Sub- Carrier Allocation (DSA), the algorithm proposed in this paper aims to reduce the effect of interference, while providing fair gain and maximizing the SINR across all users. Simulation results reveal that the proposed algorithm considers self-interference that exists between multiple antennas and is capable of achieving a capacity gain for every user with improved BER performance compared to previous work.For a single user with multiple receive antennas, co-antenna interference can be the dominant source of impairment in a Spatial Multiplex Orthogonal Frequency Division Multiple Access (SM-OFDMA) system, particularly when correlation exists between spatial sub-channels. By exploiting knowledge of the channel response and combining it with Dynamic Sub- Carrier Allocation (DSA), the algorithm proposed in this paper aims to reduce the effect of interference, while providing fair gain and maximizing the SINR across all users. Simulation results reveal that the proposed algorithm considers self-interference that exists between multiple antennas and is capable of achieving a capacity gain for every user with improved BER performance compared to previous work

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                          Nordin, R., Armour, S. M. D., & McGeehan, J. P. (2009). Overcoming self-interference in SM-OFDMA with ESINR and dynamic subcarrier allocation.In IEEE 69th Vehicular Technology Conference 2009 (VTC Spring 2009),Barcelona. (pp. 1 - 5). Institute of Electrical and Electronics Engineers(IEEE). 10.1109/VETECS.2009.5073763Link to published version (if available):10.1109/VETECS.2009.5073763Link to publication record in Explore Bristol ResearchPDF-documentUniversity of Bristol - Explore Bristol ResearchGeneral rightsThis document is made available in accordance with publisher policies. Please cite only the publishedversion using the reference above. Full terms of use are available:http://www.bristol.ac.uk/pure/about/ebr-terms.htmlTake down policyExplore Bristol Research is a digital archive and the intention is that deposited content should not beremoved. However, if you believe that this version of the work breaches copyright law please contactopen-access@bristol.ac.uk and include the following information in your message:• Your contact details• Bibliographic details for the item, including a URL• An outline of the nature of the complaintOn receipt of your message the Open Access Team will immediately investigate your claim, make aninitial judgement of the validity of the claim and, where appropriate, withdraw the item in questionfrom public view.Overcoming Self- Interference in SM-OFDMA with ESINR and Dynamic Subcarrier Allocation4. METHODOLOGYACKNOWLEDGEMENT: Travel grant for this conference is funded by University of Bristol’s Alumni Foundation, whose financial support is gratefully acknowledged.1. INTRODUCTION6. RESULTS& ANALYSIS7. CONCLUSIONSRosdiadee Nordin, Dr. Simon Armour and Prof. Joe McGeehanCentre for Communications Research Laboratory, University of BristolRosdiadee.Nordin@bristol.ac.uk-4 -2 0 2 4 6 8 1000.10.20.30.40.50.60.70.80.91Normalised perceived ESINR metric (dB)Probability(Normalised perceived ESINR metric > abscissa  Channel GainESINRRandom0 100 200 300 400 500 600 700-35-30-25-20-15-10-5051015Subcarrier indexNormalised ESINR metric (dB)  Channel GainDSADSA-ESINR-2 0 2 4 6 8 10 12 14123456789Eb/N0 (dB)Throughput (bps/Hz)  ESINR-DSAESINR-RandomESNR-DSAESNR-RandomCapacity bound0 5 10 15 20 2510-610-510-410-310-210-1100SNR(dB)BER  RandomChannel GainESINRPunctured ½ rate convolution code, constraint length 7, {133, 171}octalChannel Coding12.00 µsTotal symbol duration10.24 µsUseful symbol duration97.656 KHzSubcarrier spacing768Useful Subcarriers1024FFT Size100 MHzBandwidth5 GHzOperating frequency4.9721.8820.6061Variance-1.3361.6581.815Mean (dB)RandomChannel gainESINRAlgorithm3. SYSTEM MODEL5. SIMULATION PARAMETERS Combined benefits of MIMO architecture and OFDM(A) technology make it an attractive technology for future wireless communications, such as 3GPP LTE, WiMAX (802.16) and MBWA (802.20). However, MIMO suffers from severe impairment effects due to Co-channel interference (CCI), also known as self-interference, inherent in the MIMO architecture. This research aims to minimized the CCI effect in the MIMO-SM scheme by exploiting the knowledge of the channel gain and combine it with Dynamic Subcarrier Allocation (DSA).   Utilise the multi-user channel to mitigate channel fading and exploit it as a source of diversity. Consider the interference that exists between the spatial sub-channels. Provide fair benefit across all users. 2. OBJECTIVES From the CCDF comparison, DSA-ESINR metric outperforms the random allocation strategies by up to 7 dB. DSA-channel gain has slightly lower gain; approximately 2 dB compared to DSA using the ESINR metric. DSA-ESINR have consistent high ESINR metric compare to DSA-channel gain. This shows that DSA-channel gain does not consider the effect of self-interference (and its impact upon system capacity and BER) for the selected subcarriers.  Using a 2 × 2 SM-OFDMA system, with 64 QAM, ¾ rate modulation scheme in an uncorrelated Rayleigh fading channel environment. Adopted ETSI-BRAN Channel Model ‘E’, which simulates typical large open space outdoor environments for NLOS conditions, with excess delay spread of 1760ns, sampling period of 10ns and RMS delay spread of 250ns. Average data rate performance for both the ESINR and ESNR systems increases at approximately twice the capacity of random subcarrier allocation. The proposed algorithm is able to achieve the desired balance between fairness and system capacity. DSA using the ESINR metric has the smallest variance and the largest average channel response, compared to other types of allocation algorithm.  It shows that the proposed algorithm offers fairness whilst maximizing the average channel gain of any given user without minimizing it in the other users. DSA-ESINR has better BER performance than DSA-channel gain by 4 dB (at 10-5 BER). This implied that DSA-ESINR has the benefit of minimizing the effect of self-interference, resulting in improvement of BER.2 4 6 8 10 12 14 16051015User numberVariance  Channel GainESINRRandom2 4 6 8 10 12 14 16-4-2024User numberAverage (dB)  Channel GainESINRRandom Simulated for 16 MS users and each MS is allocated a single sub-channel consisting 48 useable subcarriers.  2000 independent identically distributed (i.i.d.) quasi-static random channels samples per user. Initial investigation has revealed that the next generation of wireless system would be capable of improving the capacity performance while providing fair gain and improved BER performance.  The proposed algorithm considers co-antenna interference within a SM-OFDMA system.  Future work will focus on investigating BER and capacity performance of the system in correlated channels where the effect of self-interference is more dominant. Based on the initial results, the proposed algorithm can be expected to provide even greater benefits.Vehicular Technology SocietyAllocation strategyESINR metric has the knowledge of self-interference from the other transmitted data stream within the same channel.DSA allows user with the lowest ESINR to have the ‘best’ ESINR that is available for the next transmission.(1) InitializationSet ESINRk,m’=0 for all users, k=1…K; Set allocated subcarrier index, Ck,s,m’=0 for all users, whereby s is the allocated subcarriers and spatial sub-channels, m’={1,2,M’}; Set s=1(2) Main ProcessWhile Nm’≠0Nsub={ (a) Sort users, start with users with less ESINR metric. Find user   k satisfying: ESINRk,m’≤ ESINRi,m’, for all i, 1≤i≤ K(b) For the user k in (a), find subcarrier n satisfying:Γk,m’≥ Γj,m’, for all j∈ N(c) Update ESINRk,m’, N and Ck,s,m’with the n from (b) according to ESINRk,m’= ESINRk,m’+ Γk,m’Nm’=Nm’-nCk,s,m’=ns=s+1(d) Go the next user in the short list define in (a), until all users are allocated another best subcarrier, Nm’=0 }

Overcoming self-interference in SM-OFDMA with ESINR and dynamic subcarrier allocation

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Overcoming self-interference in SM-OFDMA with ESINR and dynamic subcarrier allocation

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