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

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R. Nordin, S. Armour, J.P. McGeehan, “Overcoming Self-Interference in SM-OFDMA with ESINR and Dynamic Subcarrier Allocation”, Proceedings of IEEE 69th Vehicular Technology Conference, 2009. VTC Spring 2009, pp. 1-5, Apr. 2009

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

  1. 1. Overcoming Self- Interference in SM-OFDMAVehicular Technology Society with ESINR and Dynamic Subcarrier Allocation Rosdiadee Nordin, Dr. Simon Armour and Prof. Joe McGeehan Centre for Communications Research Laboratory, University of Bristol Rosdiadee.Nordin@bristol.ac.uk1. INTRODUCTION 5. SIMULATION PARAMETERS  Combined benefits of MIMO architecture and OFDM(A) technology make it an  Using a 2 × 2 SM-OFDMA system, with 64 QAM, ¾ rate modulation attractive technology for future wireless communications, such as 3GPP LTE, scheme in an uncorrelated Rayleigh fading channel environment. WiMAX (802.16) and MBWA (802.20).  Adopted ETSI-BRAN Channel Model ‘E’, which simulates typical large open  However, MIMO suffers from severe impairment effects due to Co-channel space outdoor environments for NLOS conditions, with excess delay spread interference (CCI), also known as self-interference, inherent in the MIMO of 1760ns, sampling period of 10ns and RMS delay spread of 250ns. architecture.  This research aims to minimized the CCI effect in the MIMO-SM scheme by  Simulated for 16 MS users and Operating frequency 5 GHz exploiting the knowledge of the channel gain and combine it with Dynamic each MS is allocated a single sub- Bandwidth 100 MHz Subcarrier Allocation (DSA). channel consisting 48 useable FFT Size 1024 Useful Subcarriers 768 subcarriers. Subcarrier spacing 97.656 KHz 10.24 s  2000 independent identically Useful symbol duration2. OBJECTIVES distributed (i.i.d.) quasi-static Total symbol duration 12.00 s Punctured ½ rate convolution code, Channel Coding  Utilise the multi-user channel to mitigate channel fading and exploit it as a source random channels samples per constraint length 7, {133, 171}octal of diversity. user.  Consider the interference that exists between the spatial sub-channels. 6. RESULTS& ANALYSIS  Provide fair benefit across all users. Probability(Normalised perceived ESINR metric > abscissa 1 Channel Gain 0.9 ESINR 0.8 Random  From the CCDF comparison, DSA-ESINR3. SYSTEM MODEL metric outperforms the random allocation 0.7 0.6 strategies by up to 7 dB. H1 Transmitter at X1 X1 With index 1 Tx1 channel 1 Rx1 0.5  DSA-channel gain has slightly lower gain; Base Station X1 OFDM H3 channel 3 0.4 approximately 2 dB compared to DSA using User k Input With index 2 H2 0.3 the ESINR metric. Data Scrambling/ FEC/ Symbol Serial to Parallel& DSA channel 2 0.2 Puncturing/ Interleaving Mapping Spatial Multiplexing mapping X2 Tx2 X1X2 With index 3 0.1 H4 Rx2 X2 X2 OFDM channel 4 0 -4 -2 0 2 4 6 8 10 15 With index 4 Normalised perceived ESINR metric (dB) Index 1 2 3 4 10 ESINR from DSA Index 4  DSA-ESINR have consistent high ESINR 5 Normalised ESINR metric (dB) Index 3 other users Scheme Index 2 metric compare to DSA-channel gain. 0 Index 1 -5  This shows that DSA-channel gain does ESINR1 ESINR2 -10 not consider the effect of self- G2 interference (and its impact upon system -15 ESINR G1 capacity and BER) for the selected -20 Receiver at calculation G2 DSA Mobile Station k MMSE info. G1DSA subcarriers. -25 Channel Gain -30 DSA S1 Y1 DSA OFDM DSA-ESINR Deinterleaving/ S1 S2 Parallel to MMSE Demapping -35 User k Symbol 0 100 200 300 400 500 600 700 Depuncturing/ Viterbi Serial& De- Linear 0 Output Data Demapping Y2 10 Subcarrier index Decoding/ Descrambling Multiplexing S2 Detection DSA Random Demapping OFDM 10 -1 Channel Gain ESINR  DSA-ESINR has better BER performance than DSA-channel gain by 4 dB (at 10-5 BER). 4. METHODOLOGY 10 -2  This implied that DSA-ESINR has the benefit of minimizing the effect of self- BER -3 10 Allocation strategy interference, resulting in improvement of 10 -4 BER. ESINR metric has the knowledge of self- DSA allows user with the lowest ESINR to 10 -5 interference from the other transmitted have the ‘best’ ESINR that is available for the -6 4 Channel Gain ESINR 10 data stream within the same channel. next transmission. Random Average (dB) 0 5 10 15 20 25 2 SNR(dB) 0 -2 (1) Initialization  DSA using the ESINR metric has the -4 2 4 6 8 10 12 14 16 smallest variance and the largest average User number Set ESINRk,m’=0 for all users, k=1…K; Set allocated subcarrier index, channel response, compared to other 15 Channel Gain Ck,s,m’=0 for all users, whereby s is the allocated subcarriers and types of allocation algorithm. ESINR Random 10 spatial sub-channels, m’={1,2,M’}; Set s=1 Variance  It shows that the proposed algorithm 5 (2) Main Process offers fairness whilst maximizing the 0 While Nm’≠0Nsub= average channel gain of any given user 2 4 6 8 User number 10 12 14 16 { (a) Sort users, start with users with less ESINR metric. Find user without minimizing it in the other users. Algorithm ESINR Channel gain Random k satisfying: Mean (dB) 1.815 1.658 -1.336 ESINRk,m’ ≤ ESINRi,m’, for all i, 1≤i≤ K Variance 0.6061 1.882 4.972 (b) For the user k in (a), find subcarrier n satisfying: 9 ESINR-DSA Gk,m’ ≥ Gj,m’, for all j N 8 ESINR-Random ESNR-DSA (c) Update ESINRk,m’, N and Ck,s,m’ with the n from (b) according to 7 ESNR-Random Capacity bound  Average data rate performance for both the ESINRk,m’ = ESINRk,m’+ Gk,m’ ESINR and ESNR systems increases at Throughput (bps/Hz) 6 approximately twice the capacity of random Nm’=Nm’-n 5 subcarrier allocation. Ck,s,m’=n 4  The proposed algorithm is able to achieve s=s+1 3 the desired balance between fairness and (d) Go the next user in the short list define in (a), until all 2 system capacity. users are allocated another best subcarrier, Nm’=0 1 -2 0 2 4 6 8 10 12 14 } Eb/N0 (dB) 7. CONCLUSIONS  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.ACKNOWLEDGEMENT: Travel grant for this conference is funded by University of Bristol’s Alumni Foundation, whose financial support is gratefully acknowledged.

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