1. FMT Modulation for Wireless Communication
Santosh V Jadhav
(Advisor: Prof V M Gadre)

2. Multicarrier System: Spectral partitioning:
-sinc(f) overlapping (OFDM)
-Non-overlapping (FMT)
OFDM:
FMT :

3. Spectral characteristics:OFDM

4. Spectral characteristics:FMT

5. OFDM Communication System

6. OFDM v/s FMT: OFDM: -perfect detection(zero ISI, zero ICI) under ideal
channel and absence of noise.
-simple equalization scheme with cyclic prefix
-high spectral overlap.
FMT: -nearly rectangular frequency-domain amplitude
characteristic
-High spectral containment, negligible ICI
-The absence of the cyclic prefix, together with the
reduced no of virtual carriers
-Equalizer is required even under ideal channel
condition and absence of noise

7. Why to prefer FMT over OFDM?
-Cyclic Extension in OFDM
-> Loss of transmission efficiency
-High spectral overlap in OFDM
-> Frequency offset sensitivity
-In OFDM side lobes are just 13dB lower than the main lobe
-> Significant power leakage into adjacent band

8. FMT System: Direct implementation

9. Efficient implementation: DFT based FMT system -
FMT Transmitter-

10. Contd… FMT Receiver-

11. Prototype Design:
Prototype filter h(n) completely defines the system. Allows tradeoff between-
- No. of subcarriers
- Level of spectral containment
- Complexity of implementation and latency
Can be designed by any standard FIR filter design method. Typical design parameters are-
- fcutoff : determines the ISI per subchannel
- Stopband energy : determines ICI introduced by adjacent subchannels, so should as low as possible

12. Contd… e.g. - Blackman Window Hamming Window =14, fcutoff = 0.3/T

13. Equalization in FMT System:
Due to negligible ICI, each subchannel will be independent of adjacent channel(s) as shown -
Simplified subchannel model is –

14. Contd… Subchannel impulse response ( = 14):
So, ISI and per subchannel equalization is must.

15. Conventional Schemes and FMT specific equalizer design
-Channel is equalized by one-tap equalizer
-ISI due to transmit and receive filter need to be compensated.
Linear equalizer
DFE equalizer

16. Contd… Here estimated error – Optimality criterias –
- Zero forcing(ZF)
- MMSE

17. Simulation results with MMSE
QPSK, M=64, =14:

18. Contd…
8-PSK, TCM encoder, M=64, =14

19. Problems with conventional method and solution
The process of making hard decisions on the channel symbols actually destroys information pertaining to how likely each of the possible channel symbols might have been.
Solution-
1. Additional soft information can be converted into probabilities that each of the received symbol takes on and can be exploited by a BER optimal decoding algorithm.
2. Let decoder generate its own soft information based on the soft information received from equalizer and use it in the process of equalization. So let soft information flow in both direction between decoder and equalizer for no of iteration -> Turbo Equalizer.

20. Turbo FMT System Model:

21. Contd… Turbo Equalizer- -Channel equalized by 1-tap filter
-SISI equalizer neutralizes the effect of transmit and
receive filters.

22. Performance of Turbo equalization based schemes
MMSE-DFE(BPSK, Hamming, =10):

23. Contd…
MMSE-LE(BPSK, Hamming, =10):

24. Future Scope:
-Turbo equalization based scheme to be extended to other higher order constellations (like QPSK, QAM)
-Turbo equalizer for STBC-FMT
-Multi-user detection in FMT Systems
-Adapting the System if flat fading assumption fails