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Rohit Iyer Seshadri and Matthew C. Valenti

Soft-in/ Soft-out Noncoherent Sequence Detection for Bluetooth: Capacity, Error Rate and Throughput Analysis. Rohit Iyer Seshadri and Matthew C. Valenti Lane Dept. of Computer Science and Electrical Engineering West Virginia University iyerr, mvalenti @csee.wvu.edu. Objectives.

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Rohit Iyer Seshadri and Matthew C. Valenti

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  1. Soft-in/ Soft-out Noncoherent SequenceDetection for Bluetooth:Capacity, Error Rate and Throughput Analysis Rohit Iyer Seshadri and Matthew C. Valenti Lane Dept. of Computer Science and Electrical Engineering West Virginia University iyerr, mvalenti @csee.wvu.edu

  2. Objectives • Achieve dramatic improvements in energy efficiency and throughput for Bluetooth with a minimal increase in complexity by using: • Sequence based, noncoherent demodulator • Bit-interleaving • Soft-decision decoding • Feedback from channel decoder to demodulator • Obtain an information theoretic bound on the minimum signal to noise ratio required for reliable signaling • Bit-wise log-likelihood ratios used to compute Shannon capacity under modulation, channel and receiver design constraints • Demonstrate performance improvements over popular receivers using an extensive simulation campaign • Evaluate packet error rate (PER) and throughput performance for data medium (DM) - rate packet types SISO-Noncoherent Sequence Detection for Bluetooth

  3. Bluetooth • Low cost/ low power connectivity for wireless personal area networks • Operates in the license free 2.4 GHz ISM band • Band divided into 79 channels, each 1 MHz wide. Channels changed up to 1600 times per second • Channel symbol rate of 1 Mbps • Uses Gaussian frequency shift keying (GFSK) • M =2 • BgT =0.5 • 0.28 ≤h ≤0.35 SISO-Noncoherent Sequence Detection for Bluetooth

  4. Benchmark Bluetooth System Encoder: (15, 10) Shortened Hamming Code (SHC), single error correction code Baseband GFSK signal during kT≤ t ≤ (k+1)T GFSK phase Detector: Limiter discriminator integrator (LDI) SISO-Noncoherent Sequence Detection for Bluetooth

  5. Bluetooth System with Sequence Detection GFSK pulse shape causes adjacent symbol interference Detector: Soft-Decision differential phase detector with Viterbi decoding (SDDPD-VD), [Fonseka, 2001] Viterbi decoding can produce burst errors, which could be mitigated by bit-interleaving SISO-Noncoherent Sequence Detection for Bluetooth

  6. Bluetooth System with SISO-SDDPD SDDPD-VD forms hard estimates on code bits SISO-SDDPD generates bit-wise LLRs for the code bits LLRs from detector passed to decoder, which performs soft-decision decoding Bit-interleaved coded modulation (BICM) Additionally, soft-information can be also be fed from decoder to detector: BICM with iterative decoding (BICM-ID) No gains over BICM Behavior explained using EXIT curves SISO-Noncoherent Sequence Detection for Bluetooth

  7. SISO-Soft-Decision Differential Phase Detection • Received signal at the output of a frequency nonselective, Rician channel, before filtering r’(t, a) = c(t) x(t, a) + n’(t) • Received signal after filtering r(t, a) = c(t) x(t, a) + n(t) • Received signal phase (t, a) = (t, a) + SISO-Noncoherent Sequence Detection for Bluetooth

  8. SISO-Soft-Decision Differential Phase Detection • Detector finds the phase difference between successive symbol intervals • The GFSK pulse shape causes adjacent symbol interference • The phase difference space from 0 to 2 is divided into R sub-regions • Detector selects the sub-region Dk in which lies • The sequence of phase regions (D0, DI, …) is sent to a branch metric calculator SISO-Noncoherent Sequence Detection for Bluetooth

  9. SISO-Soft-Decision Differential Phase Detection • Let be the phase differences corresponding to any transmitted sequence • A branch metric calculator finds the conditional probabilities • Branch metrics sent to a 4-state MAP decoder whose state transition is from to • The SISO-SDDPD estimates the LLR zk for ak as SISO-Noncoherent Sequence Detection for Bluetooth

  10. FEC for Bluetooth • Bluetooth specifies 7 types of ACL packets for data transfer • 6 out of the 7 packet types use cyclic redundancy check (CRC) and ARQ • 3 out of these 6, i.e. data medium (DM1, DM3, DM5) also use a (15, 10) shortened Hamming code (SHC) for forward error correction (FEC) • The (15, 10) SHC is cyclic and described by the generator polynomial • The cyclic code can hence be expressed using a 25= 32 state trellis and decoded by running either a Viterbi or MAP algorithm over the trellis SISO-Noncoherent Sequence Detection for Bluetooth

  11. Capacity Under Modulation, Channel And Receiver Design Constraints • Channel capacity denotes maximum allowable data rate for reliable communication over noisy channels • In any practical system, the input distribution is constrained by the choice of modulation • Capacity is mutual information between the bit at modulator input and LLR at detector output • Constrained capacity in nats is; [Caire, 1998] SISO-Noncoherent Sequence Detection for Bluetooth

  12. Capacity Under Modulation, Channel And Receiver Design Constraints • Constrained capacity for the proposed system is now • In bits per channel use • Constrained capacity hence influenced by • Modulation parameters (M, h and BgT) • Channel • Detector design • Computed using Monte-Carlo simulations SISO-Noncoherent Sequence Detection for Bluetooth

  13. Performance Evaluation and Comparisons • Performance of proposed SISO-SDDPD with BICM compared against • Limiter discriminator integrator detector with hard decision channel decoding, with and without bit-interleaving: LDI-HDD • SDDPD-VD, followed by hard decision channel decoding, with and without bit-interleaving: SDDPD-HDD • SISO-SDDPD followed by soft decision channel decoding, without bit-interleaving: SISO-SDDPD-SDD • Comparisons made on the basis of • Bit error rate • Packet error rate • Throughput SISO-Noncoherent Sequence Detection for Bluetooth

  14. Bit Error Rate Comparison Scenario: Minimum Eb/No to achieve BER= 10-4. Six simulated points from top to bottom are 1) LDI-HDD 2) LDI-HDD with interleaving 3) SDDPD-HDD 4) SDDPD-HDD with interleaving 5) SISO-SDDPD-SDD 6) SISO-SDDPD with BICM Information theoretic bound for SISO-SDDPD based BICM SDDPD specifications: R=24 uniform sub-regions Channel parameters: Nonselective, Rician fading, K =2 dB Bluetooth specifications: h =0.315, DM1 packet types SISO-SDDPD with BICM gives the best BER performance SISO-Noncoherent Sequence Detection for Bluetooth

  15. Packet Error Rate Comparison Scenario: Packet error rate for DM1 packet types. SDDPD specifications: R=24 uniform sub-regions Channel parameters: Nonselective, Rician fading, K =2 dB Bluetooth specifications: h =0.315 DM1 packet types SISO-SDDPD with BICM gives the best packet error rate performance. Gain over LDI based systems = 9 dB Gain over SDDPD-HDD based systems =4 dB SISO-Noncoherent Sequence Detection for Bluetooth

  16. Throughput Comparison Scenario: Throughput for DM1, DM3 and DM5 packet types Solid curve: Systems without interleaving Dotted curve: Systems with interleaving SDDPD specifications: R=24 uniform sub-regions Channel parameters: Nonselective, Rician fading, K =2 dB Bluetooth specifications: h =0.315 SISO-SDDPD with BICM gives the best throughput performance For maximal throughput, packet type should be adaptively selected to match SNR SISO-Noncoherent Sequence Detection for Bluetooth

  17. Conclusions • An energy efficient, noncoherent receiver design investigated for Bluetooth • Soft-in/ soft-out, soft decision differential phase detector developed • BICM paradigm applied to Bluetooth • Error rate and throughput compared against LDI detector and Fonseka’s SDDPD with Viterbi decoding • SISO-SDDPD-SDD shown to outperform LDI-HDD and SDDPD-HDD • Additional gains possible with interleaving • Constrained capacity found using Monte Carlo simulations SISO-Noncoherent Sequence Detection for Bluetooth

  18. Future Work • An algorithm that designs nonuniform phase regions using received phase differences and adapts itself to varying channel conditions and GFSK parameters • Nonunifrom regions can perform better than uniformly phase regions [Fonseka, 1999] • Results in a smaller look-up table • Estimating the Rician K factor and Eb/No at the receiver using the Expectation-Maximization algorithm SISO-Noncoherent Sequence Detection for Bluetooth

  19. Complexity • Branch metric calculations in SISO-SDDPD • Metric calculations involve nonlinear functions • Pre-calculated and stored in a look-up table • Table needs to be updated once at each Eb/No • Number of states in the detector • SISO-SDDPD operates on a M2- state trellis • Number for states in the channel decoder, with soft-decision decoding • ML/ MAP decoding performed on a 32- state trellis SISO-Noncoherent Sequence Detection for Bluetooth

  20. Sensitivity to h estimation errors Scenario: Effect of incorrect estimates of h on SISO-SDDPD and LDI detectors SDDPD specifications: R=24 uniform sub-regions Channel parameters: Nonselective, Rician fading, K =2 dB Bluetooth specifications: Correct value of h =0.315 Values assumed at detector =0.28, 0.35 DM1 packet types SISO-SDDPD more robust to incorrect estimates of h SISO-Noncoherent Sequence Detection for Bluetooth

  21. EXIT Chart Scenario: EXIT chart for the SISO-SDDPD based BICM receiver SD-DPD specifications: R=24 uniform sub-regions Channel parameters: Nonselective, Rician fading, K =2 dB Bluetooth specifications: h =0.315, BgT =0.5 Detector EXIT curve predicts no improvement with BICM-ID SISO-Noncoherent Sequence Detection for Bluetooth

  22. Throughput Calculations • Throughput: Maximum achievable, one way data rate [Valenti, 2002] Nt: Total number of times a given packet must be transmitted (on an average) until it is successfully decoded Ns: Number of slots occupied per round trip, including one return slot Duration of each slot: 625 µsec Ku: Number of data bits in the packet type SISO-Noncoherent Sequence Detection for Bluetooth

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